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N O V E M B E R /D E C E M B E R 1997

CONTENTS

Marshall introduce the new risk management and the policy choices firms should
be considering.

23

Volume 79, Number 6
3

Recent Developments in
Wholesale Payments Systems

To Conclude: Keep In ation

William R. Emmons

Low and, in Principle,

Wholesale payments and settlement
systems in G-10 countries have undergone significant change in recent years.
Notably, central banks have sought to
increase the safety and reliability of
these systems. In this article, William R.
Emmons describes two approaches that
have been pursued. Significant progress
has been achieved in strengthening (or
“securing”) many existing payments
system arrangements based on net settlement. In addition, many new gross
settlement systems have been created,
and existing ones have been improved.
The article also explores why privatesector financial institutions often prefer
to upgrade and secure existing net settlement systems rather than moving to
gross settlement systems, despite central
bank preferences for the latter.

Eliminate It
Thomas C. Melzer

The U.S. economy performed well
across the board in 1997, with low
unemployment, robust economic
growth, and the lowest sustained inflation in decades. Nevertheless, the current
framework for monetary policymaking
does not ensure that inflation is down
for the count, says Federal Reserve
Bank of St. Louis president Thomas C.
Melzer in a speech reprinted here. In
this speech, Melzer argues that the
Federal Reserve ought to secure the
best environment for economic growth
by adopting multi-year inflation targets
to reduce the trend rate of inflation and
keep inflation low.

9

The New Risk Management:
The Good, the Bad, and
the Ugly
Philip H. Dybvig and

45

Using Federal Funds Futures
Rates to Predict Federal
Reserve Actions
John C. Robertson and
Daniel L. Thornton

William J. Marshall

At one time, risk management was limited
to insurance and the avoidance of lawsuits
and accidents. The new risk management
also includes using tools developed for
pricing financial options for the management of financial risks within the firm.
Trading in financial markets based on
these tools can insulate companies from
the risk of changes in interest rates, input
prices, or currency fluctuations. In this
article Philip H. Dybvig and William J.

The federal funds futures rate naturally
embodies the market’s expectation of the
average behavior of the federal funds
rate. But, as John C. Robertson and
Daniel L. Thornton explain, analysts
cannot attempt to identify Fed policy
from the behavior of the federal funds
futures rate without making somewhat
arbitrary additional assumptions. The
authors investigate the predictive accuracy of a rule based on the federal funds
futures rate from October 1988 through

N O V E M B E R /D E C E M B E R 1997

CONTENTS
August
1997 using an assumption that is
Panel
Discussion:
sufficient for partially identifying when
the market is expecting a Fed action but
not for predicting the magnitude of the
action. Their forecasting rule correctly
predicts a target change at the onemonth horizon only about one-third
of the time. They conclude that more
research is needed, especially in light of
the FOMC’s recent practice of disclosing
policy decisions immediately after
FOMC meetings.

54

Index

F E D E R A L R E S E RV E B A N K O F S T. L O U I S

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NOVEMBER/DECEMBER

1997

Thomas C. Melzer is president of the Federal Reserve Bank of St. Louis. This article is based on a speech at the School of Business
Administration of the University of Tennessee at Martin, October 28, 1997. Melzer thanks Michael Dueker for assistance in the presentation
of these remarks.

To Conclude:
Keep In ation
Low and, in
Principle,
Eliminate It

become both recession-proof and inflationproof. In this view, policymakers need not
worry about demand or inflation because
markets will keep growth strong and inflation in check. However, history suggests
caution with respect to such Panglossian
notions that “all is for the best in this best
of all possible worlds.”
We must not ignore the lessons of
the past by adopting inflationary policies
that have consistently culminated in
slowed growth and higher unemployment. The fact is that, throughout history,
efforts to use expansionary monetary
policy to squeeze more real growth out
of the economy than can be sustained
have always led to increases in the
misery index. What is the misery index?
It is the sum of the inflation, unemployment, and long-term interest rates. This
index was at an all-time high in the early
1980s, when each of these three rates
soared into the double digits. By comparison, the index is very low today, registering less than half the “misery” of the
early 1980s.
The purpose of this article is to
address the following issues: why low
and stable inflation has been good for
economic growth, how inflation uncertainty hurts our economy, and what
steps the Fed can take to make its price
stability policies credible. The appropriate monetary policy response to today’s
environment of comparatively low inflation, low unemployment, and low interest
rates is to nurture it with a credible commitment to price stability—an inflation
rate so close to zero that it ceases to be a
significant factor in long-term planning.
Only in this way can the Fed reconcile
its potentially conflicting statutory objectives of “maximum employment, stable
prices, and moderate long-term interest
rates” and realize its ultimate goal of a
rising U.S. standard of living. Let me
begin by considering the first issue I
posed earlier.

Thomas C. Melzer

T

he U.S. economy is doing exceptionally
well this year, with low inflation and
an average unemployment rate of only
5 percent. Economic growth continues
to be robust in this seventh year of the
current expansion, which started in April
1991. Since that time, the economy has
created 14 million new jobs, and inflation
has been comparatively low and stable. In
the first nine months of 1997, inflation in
the Consumer Price Index was running at
an annual rate of only 1.8 percent, which
is as close to a stable price environment
as we have seen in decades. Private-sector
forecasts, however, indicate that inflation,
as measured by the CPI, is expected to
return to its trend level of roughly 3
percent in the next year.
High employment today means that
many workers are acquiring skills and
experience that will yield benefits for the
rest of their careers. But the best thing
about the current economic good news is
that it has not been created by artificial
demands stemming from excessive money
creation. On the contrary, the low money
growth and low inflation of the current
expansion mean that long-term prospects
are not being jeopardized for the sake of
today’s prosperity.
In response to such good news, some
observers—especially those prone to hyperbole—have proclaimed a “new economic
paradigm” in which the U.S. economy has

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NOVEMBER/DECEMBER

WHY HAS LOW INFLATION
BEEN GOOD FOR THE
ECONOMY?

1

The U.S. Treasury’s issuance
of inflation-indexed bonds,
which began in January 1997,
is intended in part to help
distinguish changes in real
interest rates from changes
in expected inflation, but
substantial imprecision persists.
Campbell and Shiller (1996)
provide an international
appraisal of indexed bonds
in practice.

2

See Dewald (1986) and
Feldstein (1996) for discussion
of how inflation distorts saving
and investment.

3

Diebold, Rudebusch, and Sichel
(1993) evaluate evidence that
the age of an expansion does
not significantly influence the
probability of the onset of
recession.

1997

government securities. Even at today’s 3
percent inflation trend, the real value of a
dollar is cut in half in less than 25 years.
Although the government admittedly has
to collect taxes, the inflation tax generates
incentives for wasteful efforts to reduce
money holdings, like currency, which
depreciate through inflation. Inflation
also distorts decisions to save and invest,
since inflation-compensating interest payments and inflation-induced capital gains
are counted as taxable income. The tax
on the portion of interest payments that
is intended to adjust for inflation inadvertently enlarges the wedge between the
value of the interest paid by the borrower and the after-tax value of interest
received by the lender.2 In the case of
capital gains, significant tax burdens can
fall on transactions that have not generated
any real income—for example, when an
asset is sold at a price that has increased
only at the rate of inflation. These inflation-induced tax distortions decrease
planned savings and interfere with
capital formation.
The best way to attenuate the inflation
tax is to keep inflation low and, in principle,
eliminate it.
Third, recent business-cycle research
suggests that a stable, non-inflationary
environment, rather than one in which
monetary policy is directed at fine-tuning
real growth, may be the best contribution
monetary policy can make toward sustaining real growth. Behind the premise
of fine-tuning lies the notion of a tradeoff between inflation variability and
output variability—the idea that higher
inflation can buy more real growth in
the short run. Contemporary thinking,
however, says that inflationary variability
threatens, rather than prolongs, economic
expansions. Recessions are often the
product of particular inflationary imbalances, instead of expansions that have
simply “run out of steam.”3 An example
of an inflationary imbalance from the
mid-1980s is the excessive investment
in commercial real estate that eventually
depressed the market, taking years to
unwind.

Under the successful disinflation policies of the past 15 years, the U.S. economy
has enjoyed its most cyclically stable period
ever. Since 1982, the economy has had positive growth in all but three quarters out
of 59. By comparison, between 1969 and
1982, when inflation was trending upward,
there were 20 recessionary quarters out of
56. The current stable growth experience is
the best evidence that the Fed’s choice to
fight the double-digit inflation of the late
1970s and early 1980s has been good for
the economy. Even though we have not yet
achieved price stability as I’ve defined it, the
current 3 percent inflation trend is the best
record we’ve had since the early 1960s.
Let me briefly review the basic arguments as to why low inflation is good for
the economy. First, a stable price backdrop enables the price system to work
more efficiently than it would with high
and variable inflation. By “working
efficiently,” I mean that the economy is
not wasting resources. When the general
level of prices is comparatively stable,
decision makers can interpret changes in
dollar prices as accurate signals on which
to base decisions. In free economies, clear,
reliable signals from prices help people make
the choices that are best for them. Interest
rates, for example, represent one of the
most fundamental prices in the economy—
the rental price of capital—and the real
interest rate is a central factor in savings
and investment decisions. But market
interest rates transmit fuzzier signals about
the required real rate of interest in an inflationary climate, because observed nominal
interest rates also respond to shifts in inflation expectations.1 Accordingly, the decisions of savers and investors are distorted
in an inflationary monetary regime.
Thus, the best way to keep price signals
clear is to keep inflation low and, in
principle, eliminate it.
Second, inflation distorts decisions
because it is a hidden tax on the private
sector borne by holders of money and

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NOVEMBER/DECEMBER 1997

Because inflation has been shown to be
more volatile at higher levels, the best way to
reduce its variability is to keep inflation low
and, in principle, eliminate it.
In general, U.S. monetary policy has
succeeded in capturing many of these
benefits of low inflation during the past
15 years. I would further argue that this
success has not been an accident but,
instead, a deliberate policy choice. The
policy shift since the early 1980s to a
low-inflation regime has required a commensurate reduction in the rate of monetary expansion. Growth in the M2
aggregate averaged more than 9 percent
from 1968 to 1983, but less than half as
much—4.4 percent—from 1984 to 1997.
This experience demonstrates that the
Federal Reserve can restrain excessive
money growth and bring down the
inflation rate. Inflation control is undeniably the Fed’s responsibility because it
alone has the tools to determine the longrun rate of monetary expansion needed to
keep inflation low. Even though the inflation rate so far this year is running at less
than a 2 percent rate, there remains a good
deal of uncertainty as to whether inflation
is down for the count. Until price stability
becomes the explicit, publicly recognized,
and sole objective of monetary policy, a
degree of inflation uncertainty is bound to
persist. Let me now turn to the second of
my three questions.

asymmetry often results when inflation
has fallen to a low level at which lenders
and borrowers agree that inflation has a
greater likelihood of a substantial increase
than decrease. In such an environment,
market interest rates adjust to compensate
lenders for facing these asymmetric risks.
As a market response to uncertainty, the
inflation-risk premium resembles other risk
premiums that help people hedge against
risk. Whereas other risk premiums
respond to risks that are intrinsic to the
nature of the investment, the inflation-risk
premium hedges against an unnecessary
risk uncertainty surrounding the value of
the money that will be used to repay the
debt. Only a non-inflationary monetary
regime can eradicate this unnecessary
inflation risk and thereby deliver the
lowest sustainable real borrowing costs
to stimulate capital formation and foster
future growth.
International evidence suggests that
investors often require substantial inflationrisk premiums. After they have been burned
by inflation once, investors typically need to
see years of consistently low inflation to convince them that the risk of inflation has subsided. For the past several years, almost all
major industrial countries have had inflation rates well below 5 percent. Yet the real
borrowing costs on government securities
differ widely across countries because of
the substantial inflation-risk premiums in
countries that have a long history of inflation. Indeed, the prospect of reducing the
inflation-risk premium in their interest
rates strongly motivates Italy, Portugal, and
Spain, for example, to join the European
Monetary Union.
Much of the inflation-risk premium
in interest rates stems from the experience
that once inflation is unleashed, the process
of bringing it back down is long and painful.
As a consequence, it is even more important
for the Fed to convince the public of its
intentions to contain inflation. Reductions
in the inflation-risk premium are possible if
the Fed follows a disciplined and credible
policy to move inflation lower and keep it
that way. This brings me to the last of my
three questions.

WHY IS INFLATION
UNCERTAINTY BAD FOR
THE ECONOMY?
In addition to expected inflation,
inflation uncertainty increases nominal
interest rates because lenders demand
compensation for the risk they take
that inflation might end up higher than
expected.4 The inflation-risk premium,
which effectively raises real borrowing
costs, arises in policy regimes where credibility is imperfect. What happens is that
lenders judge that future inflation will
almost certainly not be much less than
expected, but could quite possibly be
considerably more than expected. This

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5

4

Kandel, Ofer, and Sarig
(1996) and Chan (1994)
find evidence of an inflation-risk
premium in interest rates.

NOVEMBER/DECEMBER

HOW CAN THE FED
MAKE PRICE STABILITY
POLICIES CREDIBLE?

5

Walsh (1996) discusses recent
monetary policy practice in
New Zealand.

1997

sional hearings on monetary policy, the Fed
could announce a set of multiyear inflation
targets, which would then define a course by
which inflation could gradually be reduced.
Correspondingly, policy actions would be
geared both to place inflation within that
year’s target range and to set the stage for the
following year’s target. In short, when inflation is too high—and I think even 3 percent
is too high—a specific inflation target and
stated timetable would make it easy to see
if policymakers were in fact carrying out
their responsibilities.
I would argue that announced policy
objectives in the form of inflation targets
would enhance the Fed’s credibility, because
its policy actions would be easier to interpret. In such an environment, preemptive
policy actions against inflationary pressures
could be readily understood for what they
are. If, on the one hand, people believed
that the Fed were merely acting at an early
stage to head off inflationary imbalances,
they would understand that the economy
was not in immediate danger of either a
recession or a burst of inflation. If, on the
other hand, the Fed had poor credibility and
poorly understood reasons for acting, the
public might believe that the Fed acts only
when panicked, and they might therefore
interpret any Fed action as cause for alarm.

The persistence of inflation-risk premiums in nominal interest rates—even
with inflation as low as it has been in
recent years—is an indication of imperfect
inflation credibility. A policy is credible
when it can be counted on. And a credible
non-inflationary monetary policy is one
that can be counted on to keep inflation
low. Credibility is an essential element
of a price stability policy for the simple
reason that only when people have faith
in price stability can the full range of
benefits begin to accrue. Otherwise,
interest rates will remain elevated by
an inflation-risk premium.
New Zealand is one country that had a
history of high inflation in which the central
bank appears to have rapidly acquired credibility for its new, low-inflation policies.5
There, a legislative mandate calling for price
stability through inflation targets has convinced investors that the country’s imperfect
past inflation record is not likely to recur.
Without this newly created credibility—
even with low current inflation—long-term
interest rates in New Zealand could easily
be 3 or 4 percentage points higher than
they are. By achieving a degree of credibility through inflation targets and a
legislative mandate that makes price
stability the monetary policy objective,
New Zealand has been able to reduce real
borrowing costs substantially.
I am concerned, however, that in the
United States, 3 percent inflation has
become too entrenched in people’s expectations. One argument against a move to
lower inflation is that, because of these
entrenched expectations, the transition
would be too disruptive. Indeed, a surprise attack on inflation could well lead to
a regrettable loss in output. A sound way
to change these entrenched expectations
would be to adopt an approach similar
to that of New Zealand and several other
countries. This approach involves setting
a precise inflation goal and a timetable for
achieving it. At the semiannual congres-

CONCLUSION
I have emphasized that “price stability”
is a state that must be sustained, and considered sustainable, over time. Although
CPI inflation has been running at just 1.8
percent so far this year, longer-term expectations are for roughly 3 percent a year,
and there is always a risk that inflation
could run higher. What really matters is
not merely the absence of inflation at any
given point in time, but the widespread
presence of public expectations that prices
will remain stable in the future. I believe
the best way for the Fed to achieve price
stability is to announce multiyear inflation
targets, paving the way for private plans,
contracts, and Fed policies to reinforce
each other. In this way, price stability
represents a compact with the American

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REFERENCES

people that, if upheld, could achieve lower
interest rates, eliminate the deadweight
costs of inflation, and remove inflationary
imbalances as a cause of economic downturns. For its part, the Federal Reserve
can best contribute to this compact by
confirming that it is following a price stability policy by announcing specific inflation targets and a timetable for meeting
them. A legislative mandate along these
lines would further strengthen the
compact.
I do not think the excellent performance of the U.S. economy during the current economic expansion is just a chance
occurrence. The low-inflation environment
has been an important contributing factor,
and the public should give monetary policies
that have restrained excessive money
growth their due credit for contributing to
current economic good times. The public
should also recognize that the Fed’s singleminded pursuit of price stability is the best
way it can contribute to an economic environment of sustained growth and a rising
standard of living.
Indeed, my conclusion is that the best
policy for economic growth is to keep inflation
low and, in principle, eliminate it.

Campbell, John Y., and Robert J. Shiller. “A Scorecard for Indexed
Government Debt,” Yale University, Cowles Foundation Discussion
Paper 1125, May 1996.
Chan, Louis. “Consumption, Inflation Risk, and Real Interest Rates:
An Empirical Analysis,” Journal of Business (January 1994),
pp. 69-96.
Dewald, William G. “Government Deficits in a Generalized Fisherian
Credit Market,” International Monetary Fund Staff Papers
(June 1986), pp. 243-73.
Diebold, Francis X., Glenn D. Rudebusch, and Daniel E. Sichel. “Further
Evidence on Business-Cycle Duration Dependence,” Business Cycles,
Indicators, and Forecasting, James Stock and Mark Watson, eds.,
University of Chicago Press 1993, pp. 255-80.
Feldstein, Martin S. “The Costs and Benefits of Going from Low Inflation
to Price Stability,” Monetary Policy and Low Inflation Conference,
National Bureau of Economic Research, January 11-13, 1996.
Kandel, Shmuel, Aharon R. Ofer, and Oded Sarig. “Real Interest Rates
and Inflation: An Ex-Ante Empirical Analysis,” Journal of Finance
(March 1996), pp. 205-25.
Walsh, Carl E. “Accountability in Practice: Recent Monetary Policy
in New Zealand” Economic Letter, Federal Reserve Bank of
San Francisco (September 9, 1996).

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1997

Philip Dybvig is the Boatmen’s Bancshares Professor of Banking and Finance at Washington University in St. Louis. This article was prepared
while he was a consultant at the Research Department of the Federal Reserve Bank of St. Louis. William J. Marshall is the Chief Operating
Officer of NISA Investment Advisors, L.L.C. The authors are grateful for useful suggestions from Bill Gavin, Myung-Yull Pang, and Murray
Weidenbaum.

The New Risk
Management:
The Good,
the Bad, and
the Ugly

and some policy choices firms should
be considering.
We start with a discussion of the
option-pricing tools that make the new
risk management possible, and we follow
with a stylized example of how the new
risk management ought to work. Then we
consider implementation issues, including
some general policy questions as well as
some accounting issues.

Philip H. Dybvig and
William J. Marshall

TOOLS FOR THE NEW RISK
MANAGEMENT
Starting with the famous work of Black
and Scholes (see shaded insert, next page),
option-pricing theory has been very
successful in pricing various financial
claims. The Black-Scholes model was
designed to price standard call and put
options, and it has been extended to price
all sorts of financial claims. The BlackScholes model and its extensions form
the theoretical foundation for the new
risk management.
There were option-pricing models
prior to the work of Black and Scholes,
including some models with formulas
similar to Black-Scholes. What makes the
Black-Scholes model different is that it
provides a hedging strategy that is an
investment policy with an investment
equal to the model’s option price and a
terminal value equal to the terminal
value of the option. Knowing the trading
strategy means that the model is not only
someone’s best guess; it is also possible
to profit if the model is wrong. If the
model price is lower than the price in the
economy, we can sell the option, pocket
the excess over the model price, and invest
in the hedging strategy to cover the
terminal value of the option we have sold.
If the model price is higher than the price
in the economy, we follow the hedging
strategy in reverse, taking a short position
instead of a long position and lending
instead of borrowing. In the model, the

A

t one time, risk management meant
buying corporate insurance, implementing procedures to avoid lawsuits
and accidents, and installing safety equipment. The new risk management uses
financial markets to hedge different
sources of risk within the firm. Trading
in financial markets can hedge companies
against the risk of changes in interest rates,
input prices, or currency fluctuations.
While hedging per se is not new, the scale
and diversity of hedging are far greater
than they used to be. When executed
properly, the new risk management can
be good and even essential for competition. Unfortunately, the new risk management can also be bad, wasting resources
without reducing risk and perhaps even
increasing it. The new risk management
can be ugly, generating large losses such
as those in widely publicized cases at
Barings, Metallgesellschaft, Procter and
Gamble, and other firms. In these and
many other firms, employees relatively
far from the top of the hierarchy of control
had the authority to take financial positions large enough to generate losses that
could bankrupt the firm. Thus, policies for
risk management should be put in place at
the highest level of a firm, and they should
provide for monitoring and control. The
purpose of this article is to provide an
introduction to the new risk management

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NOVEMBER/DECEMBER 1997

THE BLACK-SCHOLES OPTION-PRICING MODEL
The precursor of all modern option-pricing models was developed by Fischer
Black and Myron Scholes.† The main result is an option-pricing formula based on
simple and reasonable assumptions in a continuous-time model. The remarkable
thing about the result is that it relies on the absence of arbitrage, and part of the proof
is a formula that specifies a trading strategy in the underlying stock and the riskless
bond that will replicate the payoff of the option at the end.†† If the option is priced
differently in the economy, buying or selling the option and following either the trading strategy or the reverse of the trading strategy will make money! Using the same
sort of analysis, one can derive a trading strategy that will hedge the financial risk in a
firm’s cash flows.
Now we present the Black-Scholes formula for the price of a call option. Recall
that a call option gives the owner the right (at the owner’s option) but not the obligation to buy one share of the underlying stock at the strike (or exercise) price X specified in the option contract on or before the maturity date of the option. If the stock
price is S and the price of the bond promising to pay the amount of the strike price at
the maturity date of the option is B, the Black-Scholes price, C, of the call option is
C = S N(x1) – B N(x2),
where
x1 = log (S/B)/s + s/2,
x2 = log (S/B)/s – s/2, and
s is the standard deviation (or square root of the variance) of the stock price at
maturity, given the stock price today, and the function N () is the cumulative normal

1

Cash-flow is the accounting
notion of actual cash coming in
or out from operations. Unlike
profits, cash flow does not
include depreciation or amortization, but it does include (as
an offset) investment in capital. In our examples later, we
will treat the two the same,
although this is not appropriate
except in the case of very simple businesses that rent any
required capital.

hedge replicates the option value perfectly;
in practice, the hedge is not perfect, but it
works remarkably well. This is why the
Black-Scholes model and its progeny are
widely used in business.
The introduction of these optionpricing models and the parallel
development and maturation of liquid
financial markets have made it easier
and easier to hedge financial risks using
options, futures, futures options, swaps,
caps, collars, floors, and a variety of
other financial instruments.

this strategy is the same as insurance. For
the insured, the insurance policy makes
money in bad times (when the insurable
event occurs) and loses money in good
times (when no insurable event occurs but
the premium is paid), which reduces risk
by softening the impact of bad outcomes.
The same is true of a hedging strategy;
losing money on the hedge in good times
and making money in bad times offsets the
original cash flows, making the total cash
flow less volatile. In either strategy,
payment for the insurance can be “upfront” or “pay-as-you-go”: For hedging, as
for insurance, the arrangement of cash
flows1 accommodates the preference of the
insured. There are important differences in
taxation and regulation between hedging
using insurance and hedging using financial markets, but those are beyond the
scope of this paper.

OPTION PRICING AND
RISK MANAGEMENT
Hedging an option is an example of
risk management. Its purpose is to remove
the risk and capture the pure economic
profit of the transaction. Fundamentally,

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NOVEMBER/DECEMBER 1997

distribution function. If there is a constant, continuously-compounded interest rate,
r, and T is the time-to-maturity of the option, then B is the discounted exercise price
B = Xexp(– rT).
And, if the stock has a variance, v, per unit time, we have that
s2 = vT
is the variance of the final stock price.
In the expression for C, the first term is the stock holding in the hedge strategy,
and the second term is the bond holding (which is negative, which is a short sale or
borrowing). The main assumptions of the model are absence of arbitrage, a constant
riskless rate, continuous stock prices, and a constant variance of returns per unit of
time for the underlying stock. The intuition is that we can replicate the risk of holding the option by holding just the right portfolio of riskless bonds and the underlying
stock. For example, if at a point in time the option moves fifty cents for each one-dollar movement in the underlying stock price, then the replicating strategy would hold
one share of stock for each two options we are replicating. To hedge the value of the
option, we would short (borrow) a share of stock for each of two options. In that
case, the stock’s value change would neutralize the effect on our wealth of the
option’s price change. The hedge’s holdings in the stock and bond will change over
time and in response to stock price changes, since the sensitivity of the option value
is different when the option is in the money than when it is out.
†

Black, F. and M. Scholes, “The Pricing of Options and Corporate Liabilities” Journal of Political Economy 81, 1973, 637-54.

††

For more discussion of why this makes sense, see Rubinstein, M. and H. Leland, “Replicating Options with Positions in Stock and Cash,”
Financial Analysts Journal, (Jan-Feb 1995), pp. 113-21.

RISK MANAGEMENT IN
MANUFACTURING

Using dynamic trading strategies to
hedge financial options may seem significantly different from hedging price risk in
a firm. However, the concept is exactly the
same. A hedger is taking the other side of
the risky investment in futures or
whatever would be used to replicate the
cash flows that are being hedged.
Normally, these cash flows cannot be
hedged precisely, but the hedge can still
reduce risk significantly. For example, one
policy is to hedge the expected cash flow
conditional on the price of inputs that can
be hedged in futures markets while leaving
the remainder unhedged, which means
that the remaining risk is borne by the
stock and bond holders of the firm.
Before turning to the general policy
issues in risk management, we will
consider a typical example.

Our example considers the hedging
problem of a manufacturer that uses significant amounts of copper as an input.
(With little change in the discussion, this
input could be zinc, silver, oil, or wheat.
With a slightly greater change, the
“production” could be servicing of core
deposits in a bank, and the analysis would
provide the optimal hedging of interest
rates.) We will examine the optimal
hedging of copper price movements in the
cash flows before turning to a general discussion of policy and oversight.
In the example, expected output is
1,000 units, which will sell for $100 per
unit. The price has been committed to in
advance because of long-term contracts,

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Table 1

A Manufacturer’s Copper Price Hedge
Each section of this table shows the cash flows one year from now for the simple example of a manufacturer that is facing copper price
risk. In the example, copper prices are higher when demand for output is higher. Each section of the table illustrates a different hedging
strategy and profit (= cash flow) in three copper price scenarios. The example abstracts from taxes and sources of risk that are not related
to the price of copper. In each case, the expected profit is 1,500. The point of hedging is reducing uncertainty, not increasing average cash
flow (except indirectly, because it allows you more freedom in choosing projects).
Table 1A

Unhedged Cash Flows
Probability

Copper
Price

Units
Sold

Output
Price

Total
Sales

Copper
Expense

Other
Expenses

Profit
(Loss)

1/4
1/2
1/4

25
20
15

1,200
1,000
800

100
100
100

120,000
100,000
80,000

30,000
20,000
12,000

82,000
78,000
74,000

8,000
2,000
(6,000)

Table 1B

the spot market, the copper in the unit
might cost $25 (with probability 1/4),
$20 (with probability 1/2), or $15 (with
probability 1/4).
One obvious (and common) approach
to hedging in this context would be to
forecast demand for copper and then hedge
that amount, either by entering a fixedprice contract with the supplier or by
buying that amount of copper futures—
at a shorter maturity, if necessary, because
one-year futures are not traded or have a
very large spread. This might be a natural
outcome if hedging were performed by
buyers who were responsible for copper
procurement and whose evaluations were
based on the cost of a forecast quantity of
copper. However, choosing a useful hedge
of the entire cash flow is more subtle
than that.
Table 1 contains an elaboration of the
example. When the economy is doing
well, copper prices are high (since this
firm and other manufacturers are
demanding more copper) and so is
demand for the firm’s output. Table 1A
shows the cash flows in the absence of any
special risk management to hedge copper
price risk. Table 1B shows the result of
hedging by buying forward the expected
quantity. Ironically, this naive approach to
hedging increases risk exposure, since the
firm is already more than hedged by
increased sales when the industry is doing
well and copper prices rise. The full hedge,

Naive Hedge of the Expected
Quantity Required
This hedge might be put in place as part of the procurement
process, since it looks only at expenses. This is at best an
incomplete hedge of copper costs, since the true quantity
changes with copper prices. In our example, this naive hedge
actually increases risk, since increased sales mean profits are
high when copper prices are high.
Probability

1/4
1/2
1/4

Unhedged

8,000
2,000
(6,000)

Hedge

Net

5,000
0
(5,000)

13,000
2,000
(11,000)

Table 1C

Fully Hedged Cash Flows
A complete hedge of all the cash flows requires something
more than a simple purchase of futures, since the sensitivity
to copper prices of the unhedged profit or loss is higher when
copper prices are low than when copper prices are high.
Probability

1/4
1/2
1/4

Unhedged

8,000
2,000
(6,000)

Hedge

(6,500)
(500)
7,500

Net

1,500
1,500
1,500

but the quantity may vary around this
expectation because the contracts give customers the option to choose how much to
buy within a range. Each unit will use an
amount of copper that would cost $20
purchased forward (in a firm commitment
to buy one year from now). If purchased in

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NOVEMBER/DECEMBER 1997

Figure 1

the result of which is shown in Table 1C,
cannot be implemented by simply buying or
selling copper forward one year. However,
the full hedge can be implemented either by
buying options or by dynamic trading in
forward or futures contracts. Since this
type of strategy is typical of hedging problems, it is worthwhile deriving the dynamic
hedge and discussing its operation.

Now

6 Months

12 Months
25

22.50
20

20
17.50
15

THE DYNAMIC HEDGE

tion arrival and trading occur now, six
months in, and again in a year. (This is not
an essential simplification; while the
analysis for a practical model requires
more computations, it is conceptually no
more difficult.) At the beginning of the
year, the futures price of copper delivered
a year from now is $20. Six months from
now, the futures price will be either
$22.50, with probability 1/2, or $17.50,
also with probability 1/2. The overall price
dynamic is given in Figure 1. The price at
a node in the tree is the price paid in a
firm commitment to buy copper one year
from now. From a given node, an up or
down move is equally likely, with probability
1/2, so any given price path has probability
1/4 = 1/2 3 1/2. Consistent with Table 1,
the ending node of $20 is twice as likely as
the other ending nodes because it can be
reached by either an up move followed by
a down move (probability 1/4) or a down
move followed by an up move (probability
1/4). A final price of $25 comes only from
two up moves (probability 1/4), and a final
price of $15 comes only from two down
moves (probability 1/4).
To derive the full dynamic hedge,
the firm requires one more piece of information, which is the rate at which futures
gains or losses will be reinvested, which
we will take to be 5 percent simple interest
over six months. (Actually, the rate we
choose will not affect the hedged cash
flows in Table 1C, since increasing this
rate will result in a completely offsetting
decrease in the number of contracts we
hold over the first six months.) Holding
one futures contract at one node implies
a gain of $2.50 (given an up move) or a
loss of $2.50 (given a down move), which

To study the dynamic hedge, we need
to understand the trading opportunities
and information between now and realization of the cash flows a year from now. The
sensitivity of the firm’s value to copper
prices varies in response to the interim
information, and this changing sensitivity
should be reflected in our trades.
In the current example, we assume
that the firm is using copper futures
contracts to hedge changes in copper
prices. Futures serve the same economic
purpose as forward purchases, but they
are somewhat different logistically, since
for futures the money changes hands
immediately when the prospective value
of copper rises and falls. If we buy one
futures contract, then at the end of each
day we are given (more literally, our
margin account is credited with) the
change in futures price over the day. If
we sell (or short) one futures contract,
then we must pay the change. If the
futures price increases from $50 to $55,
then the owner of two futures contracts
will collect $10, and someone who has
sold two futures contracts will have to
pay $10. If the futures price instead
decreases from $50 to $45, the person
short two contracts collects $10, and the
person long two contracts has to pay $10.
In general, the futures price need not be
exactly equal to the price we would pay
for forward purchase, but for most
purposes we can think of the two as
being the same.2
In the actual economy, information
arrives minute-by-minute, and a firm can
trade on copper prices almost continuously
in time. For our simple example, informa-

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13

2

In fact, if interest rates are nonrandom (so re-investment rates
are known in advance),
absence of arbitrage implies
that the forward price must
equal the futures price,
although one futures contract
has more impact, since the
change in value is received up
front, while in a forward contract the change in value occurs
at maturity.

NOVEMBER/DECEMBER 1997

A SMALL GLOSSARY OF RISK-MANAGEMENT TERMS
Binomial model. The binomial option-pricing model, developed by Cox, Ross, and Rubinstein [1979],
assumes that the stock return over a short time interval has one of two values. The binomial
model is a popular alternative to the Black-Scholes model because it is flexible and easy to
implement on a computer.
Black-Scholes model. This is the original modern option-pricing model (see shaded insert on pp. 10-11).
Call option. A call option is a contract that gives the owner the right to purchase a share of the underlying asset in exchange for the contractually specified strike price (or exercise price). An
American call option can be exercised at any time before maturity, while a European call option
can be exercised only on the maturity date.
Cap. An interest-rate cap is a promise to pay the excess of an interest rate above some level in each of
a number of periods. Caps are useful for containing the risk of rising borrowing costs.
Collar. A collar combines the cash flows of buying a cap and selling a floor. It is useful for containing
the risk of rising interest rates (like a cap); including the floor gives up some profit potential
when rates fall to help to pay for the cap.
Floor. An interest-rate floor is a promise to pay the shortfall of an interest rate below some level in
each of a number of periods. Floors are useful for locking in a minimum return.
Forward contract. A forward contract gives the owner the right and the obligation to buy a specified
amount of a commodity at a specified price at some specified date in the future.
Futures contract. A futures contract is similar to a forward contract except that there is daily settlement, i.e., each day the parties to the contract exchange money representing the market-determined change in value of the contract. Daily settlement minimizes the need for credit checks
and large margin accounts (which are held as collateral), since only one day’s price variation
is at risk.
Hedge. Hedging a position (or entering a hedge) is undertaking another activity with offsetting risk.
Some common hedging instruments include insurance, futures contracts, and options.
Long position. To take a long position (or to “be long”) is to purchase an asset or futures.
Put option. A put option is a contract that gives the owner the right to sell a share of the underlying
asset in exchange for the contractually specified strike price (or exercise price). An American put
option can be exercised at any time before maturity, while a European put option can be exercised
only on the maturity date.
Short position. To take a short position (or to “sell short”) is to assume the opposite of a long position.
In the case of futures, they are simply sold in the market. Shares and other securities are borrowed (for a nominal fee) then sold in the market, with the promise of buying some shares later
to return the borrowed shares. In the meantime, the short must pay any dividends or coupons
that are due the person from whom the shares were borrowed. The cash flows for a short position are the negative of the cash flows for a long position.
Value at risk. Value at risk (VAR) is a measure and methodology for assessing risk exposure by looking at total exposure to various market-level risks. This is a useful tool, but it does not account
for residual risk that is specific to the project and not related to the market-level risks.

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Table 2

Cash Flows from the Dynamic Hedge
Futures
Price Path

Cash
Now

20-22.50-25
20-22.50-20
20-17.50-20
20-17.50-15

0
0
0
0

# Contract
Now
(1,333)
(1,333)
(1,333)
(1,33
3)

Cash in
6 months

# Contracts
in 6 months

Cash in
1 yr

Pre-hedge
Cash Flow

Hedged
Cash Flow

(3,333)
(3,333)
3,333
3,333

(1,200)
(1,200)
(1,600)
(1,600)

(6,500)
(500)
(500)
7,500

8,000
2,000
2,000
(6,000)

1,500
1,500
1,500
1,500

is reinvested until the end at the interest
rate. From this we can use simple algebra
to derive the solution. In the example,
the full hedge is implemented by the
following strategy: At the start, the firm
sells 1400/1.05 ~ 1,333 futures at the
futures price of $20. If futures go down
to $17.50, the firm increases the short
position to 1,600 contracts, while if
futures go up to $22.50, the firm reduces
the short position to 1,200 contracts.
The terminal cash flow generated by
the hedge (including reinvestment) is analyzed in Table 2. For example, the second
row shows the effects of the hedge when
prices go up and then down (from $20 to
$22.50 and back to $20). The hedge starts
with no initial cash. It shorts 1,333
contracts, and when in six months the
futures price goes up by $2.50, $2.50 3
1,333 = $3,333 is borrowed, and the short
futures position is reduced to 1,200
contracts. When the futures price falls by
$2.50, $2.5 3 1,200 = $3,000 in profits are
collected, and after payment of $3,333 3
1.05 = $3,500 on the loan, net cash from
the hedge is $3,000 – $3,500 for a loss of
$500. Added to the unhedged cash flow
in that state of $2,000 (from Table 1A),
the hedged cash flow is $1,500. The
calculations in the other states work
the same way.
We can see now that the dynamic
hedge was chosen so that the re-invested
proceeds of the hedge plus the original
cash flows are made to be the same in
every contingency. The necessary hedge
can be computed by working backward
from the end. The first two rows differ
only in the price performance over the
last period. Since the difference in prehedge cash flow for these two scenarios

is $8,000 – $2,000 = $6,000 and the difference in futures prices for the two scenarios
is $25 – $20, we require $6,000 / $5 = 1,200
contracts to replicate the cash flows or
short 1,200 contracts (the offsetting position) to hedge the cash flows. Given the
calculated hedge at the last date, the calculation at the next earlier date proceeds
in the same way, and so forth back to
the start. The entire strategy can be
computed by looking at the linear equations
implicit in Table 2, or by standard
techniques described in option pricing
textbooks.
While the model underlying the
hedge for the simple example probably
seems too simple, it is in fact similar
(except for the number of intermediate
trading dates) to the binomial models
used successfully in practice. Adding
the additional subperiods is straightforward, given modern computing
resources.

SOME FUNDAMENTAL
QUESTIONS
In the example in the previous
sections, we assumed that hedging is desirable. However, this assumption is far from
obvious, and it is useful to examine potential motives for hedging.

Why Should We Hedge?
The reason for hedging should link
back to the overall objective of the firm,
which is to create or enhance economic
value. There is a general issue of whether
the firm should maximize narrowly the
value to shareholders, the total value to all
financial claimants, or some more general

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social value to a variety of stakeholders.
This distinction will not be so important to
us; most importantly, we will assume that
taxes (governments’ claims) are not part of
what we are optimizing, and for concreteness we will speak of maximizing value to
shareholders in the firm.
The first and most obvious effect of
hedging is that it reduces the volatility of
the value received by shareholders. Unfortunately, this does not have any value for
most shareholders in a large publiclytraded firm, who hold the shares in a
well-diversified portfolio and for whom
the additional risk is unimportant. Indeed,
a conflict of interest may exist between the
majority of shareholders and large
shareholders (for example, members of the
founding family who hold 30 percent of
the shares and where holdings are undiversified): expending resources to reduce risk
may benefit the large shareholders at the
expense of the rest of the shareholders.
Management may have a similar conflict,
since risk threatens their jobs and they
may have a significant proportion of their
wealth tied up in the firm’s shares. Since
most shareholders in a publicly traded firm
would not care about the additional risk
attributable to copper price exposure, this
is not a good reason for hedging. (On the
other side of the equation, the cost of
hedging may be very small; we will
consider this consideration further in a
later section on cost issues.)
A more subtle argument for managing
copper price risk is that failure to do so may
cause ancillary damage within the firm. As
an extreme case, adverse copper price movements may push the firm into bankruptcy,
which has a number of deadweight costs to
the firm, such as payments to lawyers and
accountants and the loss of profitable future
projects. More normally, unhedged risk
exposure may tend to increase taxes, on
average: While the government receives
additional tax payments when the copper
price move is favorable, an unfavorable
move will not create a compensating tax
reduction, given that tax offsets may only be
deferred (and may even be lost). A related
tax reason for managing copper prices is

that the reduction of risk makes it possible
to maintain more leverage to reduce corporate taxes and avoid “double taxation.”
“Double taxation” is the payment of both
corporate and personal taxes on cash flows
going to equity, compared with payment of
only personal taxes on cash flows going to
debt, since interest expense is an offset to
income in the computation of corporate
income tax. While there are no personal
taxes for institutional investors—and therefore no double taxation—the parallel
argument—single taxation versus no
taxation—is valid and even more powerful
for institutions. For individuals there is at
least a possibility that the corporate tax on
equity will be offset by lower taxes at the
individual level through deferred realization
of gains or by a lower capital gains rate. For
tax-exempt or tax-deferred investors, the
extra tax is unmitigated.
A third argument for managing copper
price risk is that many firms have a policy
of smoothing earnings, and hedging can
reduce volatility in earnings. Although this
is common practice, it is hard to endorse,
since it seems to be an expenditure of the
owner’s resources to minimize the amount
of information getting out to the owners.
(In principle, smoothing earnings might be
used to eliminate temporary variations and
provide a clearer picture of long-term value,
but it seems more typical that smoothing is
intended to avoid bad-looking quarters
without necessarily distinguishing shortand long-term shocks.) This use of
hedging may make management more
comfortable and minimize criticism, but
this is not obviously in the interest of
shareholders. In some cases, hedging
could be justified by the argument that it
avoids restrictive debt covenants, but such
covenants are far from binding in all but a
small proportion of firms that smooth
earnings. More common is the opposite
extreme case, in which the internal objective of the firm is to ensure that earnings
do not fall. Hedging for this purpose may
make management comfortable—indeed
too comfortable—but it discourages profitable innovation. A related strategy for
keeping volatility of earnings small is to

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maintain a low level of financial leverage,
which implies a large voluntary tax contribution that is not in the interest of
shareholders.
A fourth argument for managing
copper price risk is to make it easier to
give managers incentives to produce
profits: By hedging risk, we can make
(for example) a division manager’s
compensation depend closely on valueadded that the manager can influence
rather than what the manager can’t
influence (the actual realization of copper
prices). This argument for managing
copper price risk implies that it may be
optimal to manage copper price risk at the
division level even if copper prices do not
represent a significant contribution to the
firm’s cash flow. Of course, this strategy
begs the question of why it can’t be done
more cheaply (for compensation purposes
only) using a paper portfolio.

hedging the firm’s entire value. If the purpose
of hedging is to eliminate sources of noise
that are beyond the manager’s control, it
may even be appropriate to hedge particular
accounting numbers used in computing
compensation rather than hedging cash flow
or economic value.

With What Instruments
Should We Hedge?
For most commonly hedged risks
(such as exposure to interest rates, foreign
exchange rates, or commodity prices),
many instruments can be used for hedging.
For example, to hedge U.S. interest rates
we can use bonds, repurchase agreements,
Treasury bond futures, swaps, caps, or collars. The choice among this set would be
determined by pricing and transaction
costs, match to hedging needs, and
accounting implications.

What Risks Should We Hedge?

Support Your Investment Banker

The question of what risks to hedge
must be subordinate to the question of why
we should hedge. If there is not a
compelling reason to hedge a particular
source of risk, then we probably should not
be hedging it. One important issue is the
sense in which we would hedge a certain
type of risk. For example, suppose we are
hedging a bank’s exposure to interest-rate
risk. Should we hedge the direct interest
mismatch of existing assets and liabilities, or
should we hedge the full economic value,
which would include the value of future
business? For example, a bank may find
that, as interest rates rise, core deposits tend
to be lost. Current accounting methods
make it hard to hedge this sort of risk
without penalty (and the risk-based capital
requirements from the Basle Accord penalize
almost all hedging because one has to
increase capital once for the underlying cash
flow and once again for the hedge). There is
a related question of whether to hedge cash
flows or value. In principle, the two are the
same (if we were to hedge cash flows far
enough out), but, in practice, hedging cash
flows out a year is much different from

A common approach of managers
planning to hedge is to turn the whole
problem over to an investment banker
who, after all, has the expertise and the
traders who can put the hedge in place and
is happy to provide “free” advice on what
to do. As in all markets, the “free” advice
is priced out in what you pay for the
hedge, and then some. To avoid paying too
much, it is best to understand how the
hedge works and how much it should cost.
Ideally, such expertise should be located
in-shop; otherwise, it is worth the expense
of hiring an expert to monitor the prices
being paid to the investment banker. In
general, competition among investment
bankers may be useful in reducing the
cost, but competition will not necessarily
produce any incentive to report when
hedging is unnecessary.

ACCOUNTANT:
FRIEND OR FOE?
Suppose we put in place the optimal
hedge computed above, using the model
for demand and option-pricing theory to

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determine the correct holding in futures to
offset the risk in the cash flows. What will
this do to our accounting statements?
In general, accounting looks at the
present and the past: Accountants favor
methods whose results are easily replicable,
especially since standard mechanical rules,
even if inaccurate, are easy to defend if
the firm has followed Generally Accepted
Accounting Principles (GAAP). Hedge
accounting is a relatively new and technical
area, and the accounting profession is
only starting to address the important
issues involved.
First of all, the hedge in question does
not seem to meet the requirements for a
hedge as stated in the GAAP. According to
FAS 80, a futures contract must be marked
to market at the end of the accounting
period unless it qualifies as a hedge. To
qualify as a hedge, (1) the futures must be
designated by the firm as a hedge, (2) there
must be underlying risk to hedge, (3) while
the assessment of risk can be done on a
centralized basis (if it is impractical to do
otherwise), the risk management must be
assessed on a decentralized basis for
specific assets, liabilities, and firm commitments, and (4) there must be a clear
economic relationship between the price of
an underlying asset, liability, or firm commitment, and a high degree of price
“correlation” must be probable. (The reference to correlation bears no relation to the
usual statistical definition of correlation:
FAS 80 makes it clear that the statistical
definition is not intended and may not
be relevant in assessing compliance. Unfortunately, FAS 80 does not make clear how
correlation should be defined.) Under
these rules, our hedge of sales certainly
does not qualify, since future sales
corresponding to use of copper in production are off balance sheet and are not firm
commitments. Even if the sales were on
the balance sheet, it is not clear whether
they would meet the vague and mysterious
requirement that correlation be probable.
Failure to qualify as a hedge often
penalizes hedging. An unqualified hedge
will typically reduce volatility of future cash
flows but increase volatility of reported

earnings. This volatility is especially
damaging when it causes violation of debt
covenants or capital requirements imposed
by regulators. Volatility of earnings may
also subject management to criticism; given
the current hysteria over derivatives, we
may want to pardon a manager who forgoes
an economically useful hedge to avoid the
appearance of “risky exposure to
derivatives.” Part of the problem is that
there seems to be no simple test, given the
current state of hedge accounting, that the
lay public can apply to distinguish risky
speculation from good hedging.
One interesting feature of the
accounting rules is that hedges that are
economically equivalent may have very
different accounting treatments. Suppose
in the example above that demand does
not depend on copper prices (putting the
same number in all of the “Units Sold”
column in Table 1A) and that we are
simply interested in hedging the input cost
at expected demand. Then it might seem
equivalent to hedge through a long-term
contract with a supplier, by buying copper
futures, or by buying shares in a company
whose share price tracks copper closely.
However, the contract with a supplier has
no impact on earnings before the actual
sale, buying copper futures is covered by
FAS 80 as discussed above, and shares in
the copper company are accounted at fair
value, but unrealized gains and losses are
unlikely to appear in earnings (FAS 115).
In each of these cases, there are various
rules, ranging from somewhat specific
(FAS 105 and 107) to incredibly vague
(FAS 119), that require a company to report
its risk exposure. FAS 119 is especially
vague; basically, it calls on companies and
accounting firms to come up with reports
that can be used as a basis for later
standards. This approach comes from a
general recognition that current reporting
practice is often misleading, and from a
paucity of good ideas on how to patch
things up. It seems that hedging tends
to magnify the problems inherent in
the accounting profession’s tension
between historical cost and mark-tomarket cost.

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It should be mentioned here that some
people have proposed universal adoption
of mark-to-market (or intrinsic value)
accounting, which is “obviously” the correct thing to do because that is a good
estimate of what the firm is actually worth,
and any hedge would be seen for what it
is. Unfortunately, it is not at all clear what
this means. For example, do we include
future sales in our valuation, and if so,
how far in the future do we go, and how
do we forecast and value the future flows?
Anyone who has been involved in capital
budgeting knows that estimates of future
cash flows are often inaccurate and may
reflect the forecaster’s optimism more than
the prospects for the firm. Even without
these conceptual problems, introducing a
whole new system of accounting is not a
trivial matter. While we note that current
accounting standards are deficient for
measuring risk, we do not claim that it is
easy to do better.
The differences in accounting
treatments of economically equivalent
hedges may allow firms to hedge in spite
of the deficiencies in the accounting
standards. Whether or not a firm that
is hedging properly can avoid looking
bad, it is clear that a firm that is not
hedging at all, or even increasing risk,
can look fine.

to assess (because they are built into
pricing) and may be much larger. On a
more esoteric point, we may also want to
include in the cost of hedging the alternative use of any capital tied up in the
investment or in margin or variation
accounts. On another subtle point, a
hedge may be more costly than it appears
if its pricing and tax treatment make it
inappropriate for the firm.
What is the marginal cost that should
be used as an input for decisions about
pricing the output? It is probably common
to use the hedged price, but in fact the
marginal cost of the commodity at the time
of use is the spot market price (assuming
an active market that was probably necessary to implement the hedge in the first
place). It is irrelevant that the price has
been locked in for a fixed quantity, since
that is sunk, and the profit will be
collected or the loss borne on the hedged
quantity however much or little is actually
used. If more is needed, the shortfall will
be purchased at the spot price. If less is
needed, the excess will be sold at the spot
price. In either case, the marginal cost is
the spot price. If the marginal cost is taken
to be the hedge price (or some average
price), value may be discarded. For
example, suppose the spot price is higher
than the hedge price. Then a computation
assuming that marginal cost equals average
price or the hedged price would understate
the true cost of buying more of the input,
and additional units could be sold when it
is more profitable to sell what can be produced from the hedged quantity of inputs.
What is the transfer price that should
be used when the commodity is procured
by one unit in the firm and used by
another? For accounting purposes, the
organization should decide up front how
profits and losses in the hedging program
will be shared. It is probably best to plan
to do so in a way that hedges cash flows in
each unit, since that will tie compensation
in each unit more directly to performance
within the manager’s influence. If sharing
of hedge profit and loss is not decided in
advance, an inherent unfairness may
result. For example, suppose the transfer

COST ISSUES
What is the cost of hedging? It is
tempting to think that the cost of the
hedge is the cost of any securities
purchased in the hedge program. In fact,
the hedge is often bundled with an investment. It is a fair investment to buy a call
option for its intrinsic value, and absent
market imperfections there is no cost in
doing so. In practice, the cost includes
transaction costs such as commissions,
bid-ask spread, and any internal costs of
trading (e.g., hiring a trader and setting up
accounting oversight). For publicly traded
contracts in liquid markets, the costs are
probably small and easy to measure. When
hedging uses custom contracts provided
by investment bankers, the costs are hard

F E D E R A L R E S E R V E B A N K O F S T. L O U I S

19

NOVEMBER/DECEMBER 1997

price is ambiguous or renegotiable. If the
transfer price is the market price when the
market price is low but a hedged price
when the market is high, the purchasing
unit gets a “free option,” and the procuring
unit loses—whether or not it is hedged.
The free option allows the unit to buy at
the hedged price or the market price,
whichever is less. The procuring unit
always loses money.

how a procurement department that is
hedging material costs may actually
make overall cash flows more variable if
input prices tend to be high when the
industry does well. Less damaging, but
probably still wasteful, is the practice in
which companies use different parts of
the firm to offset hedging or they hedge
economically irrelevant risks (such as risks
that represent an insignificant part of a
firm’s cash flow volatility). For most firms,
the benefits of centralization (better control,
economies of scale, and cost saving due to
internal netting) will outweigh the costs
(mostly the difficulty of communicating
and aggregating needs). Of course, it is a
good idea to have a formal policy in either
case, whether risk management is centralized or dispersed.
A good risk management policy
should state the goals of the hedging program. Is it the firm’s policy to hedge the
value of the firm or, alternatively, earnings
or dividends paid to shareholders, and if
so, what risks should be hedged and what
risks should be borne by the shareholders?
Should hedging be implemented on a divisional or departmental level (to improve
planning and incentive compensation)
when that hedging does not reduce the
overall variability of the firm’s value?
Should the hedging program focus on
cash flows, earnings, tax avoidance, or
something else? We do not yet have
definitive answers to these questions,
but at least a consistent policy will
minimize offsetting efforts.
One important (but probably often
neglected) aspect of a risk-management
program is the need for ex post evaluation.
Especially because these programs are relatively new, it is entirely possible to design a
program that is ineffective or that even
increases risk (like the naive hedging
strategy in our copper price hedging
example). Only retrospective analysis of
the results can verify that the program is
actually reducing risk. The retrospective
analysis should also look at any side effects
of the hedging, for example variation or
margin account payments required to
maintain the hedge.

RISK-MANAGEMENT
POLICY
Given that standard accounting procedures do not provide a particularly useful
picture of the quality of a firm’s hedging
program, it is especially important for
management to adopt and implement an
understandable and effective risk-management policy. Such a policy should specify
the goal and scope of any hedging activity,
and it should dictate the degree of centralization and the control systems. Furthermore, the policy should provide for oversight and evaluation of the effectiveness
of hedging.
A common feature of the large
publicized trading losses is a failure of
control systems. Financial firms face a
particular temptation to have inadequate
controls. Because firms want to keep
successful traders around, they may tend
to be sympathetic to traders’ insistence
that the bureaucracy should not interfere
with their work. A failure to separate the
operations and accounting functions from
trading was an essential common thread
in the recent losses of over a billion
dollars each at Barings, Daiwa Bank,
and Sumitumo. In each case, the loss
was attributable to a single trader. It is
important to devote serious talent to
the job of monitoring traders, even though
the monitoring job is less glamorous,
somewhat unpleasant, and, when things
are going well, seemingly unproductive.
Besides the scenario of speculation
under the guise of risk management, risk
management can be counterproductive
if it is too localized. To illustrate, the
example we discussed earlier showed

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NOVEMBER/DECEMBER 1997

CONCLUSION

side of the debate is the suggestion that
hedging with options is a good idea and
that most criticisms are unjustified (“Keep
Those Options Open,” M. Schewitz, Risk,
October 1995, pp. 35-36).
There are, of course, many articles in
the popular press citing specific hedging
programs gone bad. The case of Metallgesellschaft has sparked a debate among
academics, since it may be a case of miscalculation of the hedge. In particular, for
commodities subject to temporary shortages,
there is no reason to believe that price
uncertainty over longer horizons can be
hedged effectively by available traded
options at short horizons, although this is
often assumed to be the case. Several
scholars have studied this question using
theoretical tools (S. Ross, “Hedging Long
Run Commitments: Exercises in Incomplete
Market Pricing,” 1995 mimeo, Yale University), empirical tools (F. Edwards and M.
Cantor, “The Collapse of Metallgesellschaft:
Unhedgeable Risks, Poor Hedging Strategy,
or Just Bad Luck,” Journal of Applied Corporate Finance, Spring 1995, pp. 86-105, or
G. Bakshi, C. Cao, and Z. Chen, “Pricing
and Hedging Long-Term Options,” mimeo,
University of Maryland), or both theoretical
and empirical (S. Pirrong, “Metallgesellschaft:
A Prudent Hedger Ruined, or A Wildcatter
on NYMEX,” mimeo, Washington University in Saint Louis). The defense of
Metallgesellschaft’s hedging program is
that it was a textbook hedge that would
have done fine if not interrupted (C. Culp
and M. Miller, “Hedging a Flow of Commodity
Deliveries with Futures: Lesson from Metallgesellschaft,” Derivatives Quarterly, Fall 1994).
Value at risk (“An Overview of Value
at Risk,” D. Duffie and J. Pan, Journal of
Derivatives, Spring 1997, pp. 7-49) is one
methodology that is widely used in practice
to quantify various common sources of
financial risk. This methodology has its
critics, both because an objective measure
is difficult to agree upon (“VAR: Seductive
but Dangerous,” T. Beder, Financial Analysts
Journal, September-October 1995, pp. 1224), and because value at risk neglects
idiosyncratic risk and some market
sources of risk.

Risk management is an important and
difficult area of corporate policy. We have seen
news accounts of disastrous failures in risk
management. Less spectacular, but perhaps
more important, is the widespread use of
futures contracts and swaps to hedge foreign
exchange, interest rate, and commodity risks,
since, without this ability to hedge, many
profitable businesses would be too risky.
The next few years should be especially
interesting, as companies work on implementing vague new accounting standards
that require them to describe their risk
exposure. Now is also an exciting time for
the development of internal controls and
policies as companies work on developing effective hedges while avoiding
catastrophic losses.

SOME FURTHER READINGS
For general information on risk management, some banks have issued guides
that may be useful. For example, the J. P.
Morgan/Arthur Anderson Guide to Corporate
Risk Management is a primer on risk management, while The Chase Guide to Risk
Management is an extended glossary
published by Chase Manhattan in association with Risk magazine. Another good
general resource is “A Survey of Corporate
Risk Management,” which was a separately
numbered insert to The Economist,
February 10, 1996, pp. 1-22.
Some observers have debated the
advisability of hedging with options. One
criticism is that hedging with derivatives
often amounts to gambling with firm
money at the encouragement of banks
(“Betting Your Hedges,” J. Ralfe, Risk, July
1994, pp. 22-23). One good point is that
customized option positions are unlikely
to be a good value, especially if it might be
necessary to unwind the position before
maturity (“Caveat Emptor,” D. Westby,
Risk, June 1995, pp. 24-25). A different
argument is that poorly constructed strategies with poor disclosure can lead to legal
troubles for managers and directors
(“Courting Trouble,” W. Falloon, Risk,
August 1994, pp. 32-33). On the other

F E D E R A L R E S E R V E B A N K O F S T. L O U I S

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NOVEMBER/DECEMBER

1997

William R. Emmons is a research economist at the Federal Reserve Bank of St. Louis. Kent A. Koch provided research assistance.

Recent
Developments
in Wholesale
Payments
Systems

which the net amount of funds, securities,
or other financial obligations owed by or
to each participant is transferred. The
primary shortcoming of traditional “unsecured” net settlement systems is that not
only do they expose their own members
to the risk of default by other members,
they also expose financial institutions and
other creditors outside the netting system.
The danger is that liquidity or solvency
problems will thus be transmitted quickly
and unpredictably throughout the global
financial system.
“Secured” net settlement systems, on
the other hand, are designed so that any
disruptions caused by a single member
(even if this happens to be the institution
with the largest net obligations to other
members) can be absorbed by the system
and its members with no risk of further
propagation. To achieve this goal, such
systems require that members undertake
extensive and perhaps costly risk-management measures. These measures typically
include real-time monitoring of counterparties within the system, net debit caps,
collateralization, and additional openended financial guarantees in case all
other safeguards prove inadequate.
Another approach to strengthening
wholesale payments systems involves
greater private-sector use of gross settlement.
Gross settlement systems include real-time
gross settlement payments systems
(RTGS), delivery-versus-payment (DVP)
systems, and payment-versus-payment
(PVP) systems. Recently, many central
banks have created new RTGS systems or
improved their existing ones to strengthen
their wholesale payments systems. In contrast to unsecured net settlement systems,
gross settlement systems can eliminate
virtually all repercussions to other privatesector members when one institution
encounters difficulty. There is a cost, however. Depending on its structure, a gross
settlement system may impose significant
liquidity demands on participants, or it

William R. Emmons

P

ayments systems can be divided
conceptually into two components:
retail and wholesale. The retail payments system, used primarily by nonbanks for making and receiving payments,
involves relatively small transfers of monetary value. In contrast, the wholesale system, which banks use to make payments
to each other, involves relatively large
transfers.1
The Bank for International Settlements
(BIS) in Basle, Switzerland (a consultative
forum for major central banks) has recently
published a series of reports covering various aspects of the wholesale payments
system,2 the purposes of which are, first,
to inform central bankers and paymentssystem participants about current practices
in wholesale payments systems, and
second, to provide a central-bank perspective on how various changes to these
practices could enhance the safety and
efficiency of wholesale payments systems.
This article summarizes the reforms to
G-10 wholesale payments systems
documented in and spurred by this
series of BIS reports.
One general approach has been to
strengthen (or “secure”) existing payments
system arrangements based on net settlement.3 Net settlement systems accumulate
a record of financial obligations among
participants over a prespecified period of
time, such as a business day, at the end of

F E D E R A L R E S E R V E B A N K O F S T. L O U I S

23

1

See Emmons (1996) for an
overview of the retail payments
system in the United States.
See Humphrey, Pulley, and
Vesala (1996) for a comparison of G-10 countries’ retail
payments systems or Bank for
International Settlements
(1993b) for details on both
retail and wholesale components of payments systems in
the G-10 countries.

2

Bank for International
Settlements, 1989, 1990,
1992, 1993a, 1993b,
1995a, 1996, 1997a,
1997b.

3

“Secured” net settlement systems (discussed later in this
article) are those that can withstand the failure of the member
financial institution with the
largest amount due to other
members of the system.

NOVEMBER/DECEMBER 1997

may require that the central bank incur
substantial supervisory and riskmanagement costs in the process of
alleviating liquidity burdens.
Most G-10 central bankers believe
that, despite their costs, gross settlement
systems will be important components of
wholesale payments systems in the future.
Why, then, have private-sector financial
institutions very often chosen to upgrade
and secure existing net settlement systems
instead of moving more rapidly to gross
settlement systems? Could (and should)
central banks do more to facilitate a more
widespread and rapid transition to gross
settlement systems?
This article does not provide definitive
answers to these questions. Instead, I offer
an overview of recent developments in
large-value gross and net settlement systems
in the G-10 countries. In the first section,
I discuss gross settlement systems, including
RTGS systems, for large-value funds transfers; DVP systems, for securities; and PVP
systems, for foreign-exchange settlement.
The second section discusses net
settlement systems and explores several
related issues, including the risk that netting agreements may not be legally binding
in all jurisdictions and the possibility that
liquidity or solvency problems could spill
over from one financial institution to another
and, in turn, to the financial system as a
whole, creating a situation termed systemic
risk. The third section concludes with a
few tentative hypotheses regarding the relatively slow movement to date of wholesale
payments and settlement activity to gross
settlement systems.

4

The common theme in these fictional
but representative headlines is a desire on
the part of G-10 central banks to influence
private-sector behavior in wholesale
(large-value) payments systems. In particular, central banks have encouraged the
use of real-time gross settlement payments
systems and trade-by-trade securities and
foreign-exchange settlement systems. This
situation has occurred because there is
virtual agreement among major central
banks that gross settlement systems make
wholesale payments systems more immune
to widespread financial disruption, a
key determinant of economic stability
and efficiency.

Key Design Issues in Real-Time
Gross Settlement Systems
Real-time gross settlement systems
are large-value funds transfer services that
operate continuously during the business
day to provide irrevocable settlement of payments obligations in central-bank money.
Irrevocable funds transfers on RTGS systems
occur when a central bank debits the reserve
account of the payor and credits the account
of the payee. This transfer of value from
payor to payee is simultaneous and final
(i.e., not subject to reversal for any reason).
If the funds transfer occurs (at least in principle) at the time the instructions of the
payor are transmitted to the central bank,
then it is said to occur continuously, or in
“real time.”
Central banks provide RTGS systems
to commercial banks and other selected
institutions such as government agencies
and, in some countries, clearing houses for
securities and derivatives exchanges (Bank
for International Settlements, 1997a,
pp. 33-7; Bank for International Settlements,
1997b, p. 14). Funds transfers over RTGS
systems may be for millions of dollars or
the local-currency equivalent, although
these systems also handle smaller
payments.
The design and operation of RTGS
systems differ considerably from one
country to another. Two important dimensions along which currently operating or

GROSS SETTLEMENT
SYSTEMS
Fedwire (the Federal Reserve’s
Fedwire Funds Transfer
Service), TARGET (the TransEuropean Automated Real-time
Gross Settlement Express
Transfer System), and BOJ-Net
(the Bank of Japan’s largevalue funds transfer service)
are all real-time gross settlement (RTGS) systems.

“Federal Reserve Updates PaymentsSystem Risk Policy, Implements Pricing on
Fedwire.”
“European Central Banks Reach Outline
Agreement on TARGET.”
“Bank of Japan to Convert BOJ-Net to
Real-Time Gross Settlement Exclusively.”4

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NOVEMBER/DECEMBER 1997

Table 1

Intraday Credit Policies and Centrally Located Queues
in G-10 RTGS Systems
Countries Whose RTGS
Systems Provide:

Centrally
Located Queue

No Centrally
Located Queue

Central bank intraday credit

Belgium
France
Germany
Italy
Netherlands
Sweden

United Kingdom
United States

No central bank intraday credit

Switzerland

Japan

No RTGS system: Canada.
SOURCE: Bank for International Settlements (1997a, Table 3, 13).
p.

proposed RTGS systems differ are (1) policies toward the granting of central bank
intraday credit and (2) the existence and
management of queues (see Table 1).
Intraday credit is valuable in an RTGS
system because it can reduce payment
blockages that may arise as one bank’s outgoing payment awaits an incoming payment
from another bank, which may, in turn, be
waiting on a payment from a third bank.
Payments may become blocked in RTGS
systems because of the “cover principle”
in gross settlement systems: An outgoing
payment order is executed if and only if
the sending bank currently has sufficient
reserves, or cover, in its reserve account at
the central bank.
The worst case of uncoordinated
payment demands is gridlock, in which
no bank can make a payment through an
RTGS system because all reserve balances
are held by banks due to receive payments
from others. Although unlimited central
bank intraday credit would eliminate gridlock, it is not offered in any RTGS systems
because such a policy would create moral
hazard, in that banks might tend to manage
their intraday liquidity less intensively.
Such a situation could give rise to an acute
liquidity crisis, into which central banks
would need to intervene in their role as
lender of last resort. Ample availability of

central bank intraday credit could also
hamper the emergence of intraday money
markets, which are, in principle, a necessary
component of a complete and efficient set
of financial markets.
Despite the fact that all RTGS systems
are capable of operating continuously,
some payment orders in some RTGS
systems are not carried out immediately.
For example, when a sending bank has
insufficient funds in its reserve account
and central bank intraday credit is not
available, either temporarily for that bank
or as a matter of system design, a payment
order will not be executed. A pending
payment order is subject to two different
responses by central banks.
The payment order may be rejected
outright, in which case the sender may
enter it into an “internal queue” that assigns
priority to outgoing payments. Selected
payment orders are then resubmitted to
the RTGS system when sufficient covering
funds in the bank’s reserve account become
available, either from payments received or
via borrowing from another bank.
Alternatively, a payment order that
cannot be executed because of insufficient
reserve funds may enter a “centralized
queue” maintained by the central bank.
That is, rather than returning the payment
order unexecuted to the sending institution,

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NOVEMBER/DECEMBER 1997

Table 2

Real-Time Gross Settlement (RTGS) Systems in G-10 Countries
Country
Belgium
Canada
France
Germany
Italy
Japan
Netherlands
Sweden
Switzerland
United Kingdom
United States

Name of RTGS System

Year of Implementation

ELLIPS
—
TBF
EIL-ZV
BI-REL*
BOJ-NET
TOP*
RIX
SIC
CHAPS
Euro version of CHAPS
Fedwire

1996
—
1997
1988
1997
1988
1997
1986
1987
1984
1999
1918

*BI-REL and TOP replace previously existing RTGS systems BISS (implemented in 1989) and FA (implemented in 1985), respectively
(Bank for International Settlements, 1993b, pp. 218-19, 302-5).
SOURCE: Bank for International Settlements(1997a), Annex 1.

the central bank may retain all payment
orders that require incoming cover in a
centrally located computer file. When
adequate reserves become available to
execute any of the queued requests, the
central bank then reenters the payment
order into the system.
A centrally located and managed queue
can facilitate an orderly flow of payments
because the system operator can identify
payment requests that will offset each other
to some extent. That is, one payment provides cover for the next, which provides
cover for the next, and so forth. This type
of oversight and queue management is
termed “optimization” (Bank for International Settlements, 1997a, pp. 24-7).
The existence of a centralized queue
and optimization routines may discourage
intensive management of intraday liquidity
by banks. These factors may also encourage
banks to anticipate payments (i.e., credit
the accounts of depositors to whom
queued payments are directed before final
settlement actually occurs). Unfortunately,
the practice of systematically anticipating
payments that are being held in queues

tends to increase systemic interdependence and settlement risk—precisely
the problems that RTGS systems are
designed to eliminate.

Overview of RTGS Systems
in G-10 Countries
Table 2 lists the RTGS systems
currently in operation or in preparation in
the G-10 countries. The Federal Reserve’s
Fedwire funds-transfer service is the oldest
RTGS system in the world. Since 1984,
RTGS systems have been introduced by all
other members of the G-10 group of countries except Canada. Other European
Union countries (i.e., those not in the
G-10) that hope to participate in the initial
launch of the European single currency in
1999, including Greece, Spain, Ireland,
Luxembourg, Austria, Portugal, and
Finland, are also developing RTGS systems
(European Monetary Institute, 1996).
Other countries that have recently introduced RTGS systems or plan to do so
include the Czech Republic, Hong Kong,
Korea, Thailand, Australia, China, New

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26

NOVEMBER/DECEMBER 1997

Zealand, and Saudi Arabia (Bank for International Settlements, 1997a, p. 1).
The Federal Reserve System’s Fedwire
Funds Transfer Service (commonly known
as “Fedwire”) began operations in 1918
and was converted to a fully computerized,
high-speed electronic telecommunications
and processing network in 1970 (Bank for
International Settlements, 1997a, p. 12).
In addition to upgrading Fedwire technical
and communications capabilities, the Federal Reserve has also implemented a series
of measures to improve Fedwire risk management in recent years (Richards, 1995;
Hancock and Wilcox, 1996). One focus
of these efforts has been to reduce banks’
daylight overdrafts (short-term credit
extensions by the central bank) on Fedwire
(see shaded insert: “Federal Reserve
Attempts to Limit Daylight Overdrafts
on Fedwire”).
RTGS systems in European G-10
countries differ among themselves but
generally fall into two categories according
to whether or not the country expects to
participate in the initial phase of European
Economic and Monetary Union (EMU)
beginning in 1999. Those countries that
plan to participate (Belgium, France, Germany, Italy, and the Netherlands) have
conformed their RTGS systems to a
common set of standards to facilitate their
interlinking in the TARGET system. In
particular, fairly liberal policies toward
central bank intraday credit and centralized
queuing facilities are envisioned for participating RTGS systems. Both of these
features enhance the liquidity of RTGS systems. In contrast to Fedwire, the EMU
systems will not assess charges for daylight
overdrafts, although such borrowings must
be fully collateralized in order to protect
the fledgling European System of Central
Banks against credit risk posed by individual
banks (Bank for International Settlements,
1997a, pp. 12-3). European G-10 members
that do not plan to participate in EMU at
the outset include the U.K. and Switzerland
(not a member of the European Union).
Liquidity-enhancing measures in these
countries’ RTGS systems (particularly in
Switzerland) are not as liberal as those of the

other European countries mentioned above,
as the discussion below will make clear.
The Bank of Japan is prepared to go
further than any other G-10 central bank
in forcing the pace of change toward RTGS
systems. Currently, banks may submit payments to BOJ-Net to be settled at 9:00 a.m.,
1:00 p.m., 3:00 p.m., or 5:00 p.m. on a net
basis; or payment orders may be submitted
for immediate execution via the RTGS mode
of BOJ-Net. The BOJ announced at the end
of 1996 that it will phase out the net settlement capability of BOJ-Net by the year
2000 (Matsushita, 1997). Thereafter, realtime gross settlement will be the only mode
of settlement available via BOJ accounts.
This is a significant policy decision, because
designated-time net settlements accounted
for 98.8 percent of volume and 99.9 percent
of value on BOJ-Net in 1995, while RTGS
accounted for the remainder (Bank for
International Settlements, 1997a, Annex 1).
In sum, there appears to be no
international consensus regarding the
optimal design of an RTGS system. This
conclusion is not surprising when one
takes into account significant cross-country
differences in central-bank preferences,
locally prevailing cash-management technologies, availability of collateral, and
securities market liquidity (Furfine and
Stehm, 1996). All existing systems appear
to be a compromise between objectives
that sometimes conflict among banks and
institutional constraints that may be evaluated differently by different central banks.

The Role of Intraday Credit
in RTGS Systems
Some RTGS systems allow participating banks to send payments with
finality for amounts greater than their
reserve balances immediately prior to the
time of the request. In carrying out such a
payment request, the central bank extends
a short-term loan to fund the reserve
account of the sending bank. Since all
central banks that grant such credit extensions require repayment by the end of the
business day, these loans are termed
daylight overdrafts.5

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27

5

All G-10 central banks provide
overnight lending facilities,
some of which can be accessed
during the business day (Bank
for International Settlements,
1997a, Annex 1, Part II).
Without exception, banks find
them relatively unattractive
sources of intraday funds to
meet payments obligations.

NOVEMBER/DECEMBER 1997

FEDERAL RESERVE ATTEMPTS TO LIMIT DAYLIGHT
OVERDRAFTS ON FEDWIRE

6

Each bank’s net debit cap is set
as a multiple of its regulatory
capital. Branches or agencies
of foreign banks, domestic
financial institutions that have
been identified by the Federal
Reserve as troubled, and nonbank financial institutions that
provide overdraft funding of
securities activities for affiliates
must provide collateral for
overdrafts. Collateral is also
required for frequent and material overdrafters. In total, more
than 50 percent of all intraday
credit extended by the Fed is
collateralized.

For more than 10 years, the Federal Reserve has undertaken a campaign to induce
banks to control the amount of their daylight overdrafts on Fedwire. While Fed policymakers have long believed that relatively liberal provision of central bank intraday
credit on Fedwire was appropriate (Board of Governors of the Federal Reserve System,
1988, p. 50), they also felt that some form of market discipline or regulatory restraint
on daylight overdrafts could improve the allocative efficiency of such credit without
sacrificing its overall benefits in terms of enhancing the system’s liquidity. The impetus
for Federal Reserve action to limit Fedwire daylight overdrafts stemmed from three
considerations (Richards, 1995, pp. 1066-67):
First, large daylight overdrafts create the potential for large demands for overnight
borrowing, thereby complicating the conduct of monetary policy. Since daylight overdrafts are unsecured, but overnight discount-window loans must be secured, a large
overhang of daylight overdrafts that cannot be repaid by day’s end could create disorderly conditions in the Federal funds and securities markets as reserves and collateral
are sought to eliminate or secure Federal Reserve lending. Alternatively, such an overhang could force the Fed to allow some uncollateralized overnight overdrafts, thus violating its own risk-management policies.
Second, the Fed became increasingly aware in the 1980s of the substantial credit
risk associated with unsecured daylight overdrafts. The Fed guarantees all payments
made on Fedwire, so unlimited, unpriced, unsecured overdrafts allowed sending banks
to appropriate the Federal Reserve’s unsurpassed credit rating at no cost to themselves.
Finally, the Fed began to recognize more clearly that substantial daylight overdrafting on private large-value transfer systems put the payments system as a whole at risk.
In order to control the risk in parts of the wholesale payments system over which the
Fed had only indirect influence—such as CHIPS † —it was necessary to accumulate
experience and demonstrate progress in managing risk where the Fed did maintain control, namely, on Fedwire. Controlling daylight overdrafts on Fedwire was a step toward
implementing sound intraday credit policies throughout the wholesale payments system.
The Federal Reserve imposed net debit caps on daylight overdrafts in March 1986
and began charging explicit fees for daylight overdrafts in accounts at Federal Reserve
Banks in April 1994 (Hancock and Wilcox, 1996, pp. 873-76). The Fed also uses realtime monitoring for “problem” institutions and requires these and selected other
Fedwire participants to post collateral for daylight overdrafts. Net debit caps were
tightened and adjusted several times subsequent to their introduction, while overdraft
fees were increased in April 1995. Empirical evidence indicates that these measures—
especially overdraft fees—have been effective in curtailing certain banks’ use of daylight overdrafts on Fedwire (Hancock and Wilcox, 1996, pp. 906-7).
†

CHIPS (Clearing House Interbank Payments System) is a net settlement system operated by the New York Clearing House Association.
See section on net settlement systems for details.

There are several basic models for daylight overdraft privileges on RTGS systems
(Bank for International Settlements,
1997a, pp. 14-21 and Annex 1, Part II).
Among G-10 RTGS systems, daylight over-

drafts on Fedwire within a bank’s net debit
cap are unusual in that they do not generally require specific collateral backing.6
This feature is advantageous to banks,
which typically do not need to hold or

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28

NOVEMBER/DECEMBER 1997

manage significant amounts of reserves or
collateral for payments purposes. Another
unusual aspect of daylight overdrafts on
Fedwire is explicit volume-based pricing.
The Federal Reserve charges an annualized
rate of 15 basis points on the daily average
overdraft in excess of the bank’s deductible
amount, which is 10 percent of its regulatory capital. Only about 90 financial
institutions typically incurred overdraft
fees of more than $100 in any two-week
period in 1995 (Richards, 1995, p. 1072),
so Fedwire overdraft fees do not constitute
a large explicit cost for intraday credit.7
Given the existence of some amount of
unpriced, uncollateralized daylight credit
(up to the amount of a bank’s net debit cap)
and low explicit costs for overdrafts in excess
of an institution’s deductible, the Federal
Reserve’s intraday credit policy could be
termed relatively liberal, overall.
Another model for central-bank extensions of intraday credit requires a borrowing
bank to fully collateralize the overdraft.8
This approach is typical of existing or
planned European RTGS systems that
permit daylight overdrafts. Quantitative
limits on overdrafts apply in Belgium and
Italy but not in Germany, the Netherlands,
or Sweden (Bank for International
Settlements, 1997a, Annex 1, Part II).
Intraday credit in the U.K. and France
is available not through daylight overdrafts
per se but rather through intraday sale and
repurchase transactions with the central
bank. This facility was chosen in order to
solidify the central bank’s legal claim to the
securities involved, rather than for any
substantive economic reason (Bank for
International Settlements, 1997a, p. 13).
Similarly, daylight credit for the RTGS
component of Japan’s BOJ-Net is available
exclusively through sale and repurchase
transactions. Given adequate supplies of
collateral securities in the market, a central
bank intraday credit policy involving
unpriced but collateralized overdrafts
(or sale and repurchase transactions)
with no quantity limits or relatively high
ones could also be termed fairly liberal,
as is the case in the United States on
Fedwire.

Finally, Switzerland alone among the
G-10 countries with RTGS systems operates
a very restrictive policy toward central
bank intraday credit. The Swiss SIC system
does not allow daylight overdrafts on any
basis (collateralized or not), nor does the
Swiss National Bank (SNB) provide facilities for intraday sale and repurchase
agreements. The only direct liquidity
assistance provided by the SNB is collateralized overnight borrowing, which, if it
arises from a failure by a bank to produce
reserve funds to cover its queued payments,
incurs a penalty rate of interest (Bank for
International Settlements, 1993b, p. 365).

Delivery-Versus-Payment (DVP) and
Payment-Versus-Payment (PVP)
Systems
As part of their overall function of
providing bank-to-bank large-value funds
transfers, RTGS payments systems frequently
constitute one element in a delivery-versuspayment (DVP) settlement system for
securities or of a payment-versus-payment
(PVP) system for settling foreign-exchange
trading obligations (Bank for International
Settlements, 1992, p. 15; Bank for International Settlements, 1993a, p. 4). Many
new DVP and PVP systems are being
planned or discussed (Bank for International
Settlements, 1997a, pp. 33-7). The three
main models for structuring DVP systems
are outlined below (Bank for International
Settlements, 1992, pp. 17-24):

7

Richards reports that a much
larger group of banks–about
700–appear to keep their
overdrafts within their net debit
caps by managing intraday liquidity, thereby avoiding overdraft fees, as well (Richards,
1995, p. 1072).

8

To be fully effective as a central
bank risk-management tool in
a real-time payments system,
a full-collateralization policy
requires a “Model 1” DVP system covering collateral-eligible
securities. The point is that
collateral securities must be
available for real-time pledges,
as well.

Model 1 DVP Systems. So-called

“Model 1” DVP systems consist of linked
gross, simultaneous settlement of a securities transfer (delivery) and the corresponding funds transfer (payment). The Federal
Reserve’s Securities Transfer Service for
U.S. Treasury and agency securities (commonly called the “Fedwire book-entry system”) operates on the same principle as
RTGS systems for funds transfers. That is,
the seller of a security (comparable to the
payor of funds above) must post the securities with the system operator before the
buyer of the security (comparable to the
funds payee above) takes final, irrevocable

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NOVEMBER/DECEMBER 1997

delivery. The Fed’s book-entry and funds
transfer services are linked and together
constitute a “Model 1” DVP system (Bank
for International Settlements, 1992,
pp. A322-24).
A few other central banks have developed already or are in the process of
developing similar systems. In particular,
“Model 1” DVP systems will be critical in
providing the future European System of
Central Banks (ESCB) with the protection
against individual banks’ credit risk
envisioned in the policy of collateralized
intraday overdrafts. Although the ESCB
has no plans to establish a Europe-wide
gross settlement system for securities, individual national securities settlement
systems will be linked with TARGET to
establish “Model 1” DVP systems.

States, and Euroclear and Cedel Bank in
Europe (Federal Reserve System, 1997).
As noted above, final settlement of the net
obligations on one or both legs (securities
and/or funds) that arise in these systems is
typically accomplished via a gross settlement system. This points out an important
complementarity that often exists between
the various types of DVP systems. “Model
3” DVP systems in the U.S. may use the
Federal Reserve’s “Model 1” DVP system to
provide final settlement of the net obligations of participants resulting from multilateral clearing of securities or funds
transfers.
PVP Systems. PVP systems are analogous

to “Model 1” DVP systems because they
allow a pair of financial transfers to be settled on a gross basis simultaneously and
with finality. The difference is that each
leg of a PVP transaction consists of a funds
transfer on a different national RTGS
funds-transfer system. That is, instead of
providing simultaneous transfers of securities and funds in a single currency, PVP
systems allow foreign-exchange transactions to be settled with finality in real time.
PVP systems could be helpful in reducing
foreign-exchange settlement risk, the
single largest remaining source of risk
in G-10 payments and settlement systems
(Bank for International Settlements,
1996, pp. 4-5).
No PVP systems linking national
RTGS systems are currently operating or
being planned. Although it may appear to
be such a system, the TARGET system in
Europe will link national RTGS systems
operating in the same currency, the euro.
Therefore, TARGET’s interlinking of
national RTGS systems will constitute a
communications and clearing system only
(similar to the financial links between
the twelve Federal Reserve Banks in the
United States).
Private foreign-exchange netting
arrangements provide PVP elements
without central-bank involvement in a
manner analogous to a “Model 3” DVP
system. For example, in a multilateral foreign-exchange netting arrangement that

Model 2 DVP Systems. A “Model 2”
DVP system consists of gross settlements
of securities transfers, followed at the end
of the day by net settlement of funds transfers. The U.K. gilt-edged (Treasury) securities market operated according to this
model as of 1992 (Bank for International
Settlements, 1992, pp. A319-21). In this
type of system, all securities transfers are
final when executed during the day.
However, the corresponding funds transfers remain provisional until the end of the
day, when final settlement occurs on a
multilateral net basis. Failure of a bank in
a net-debit position on funds transfers (i.e.,
owing funds after calculation of net funds
positions) does not affect the finality of
securities transfers that have already
taken place.
Model 3 DVP Systems. Finally, “Model

3” DVP systems consist of parallel multilateral net settlement of securities and funds
transfers. The Bank of Japan’s “DVP-NET”
is an example of this type of system (Bank
for International Settlements, 1992,
pp. A37-9). Private DVP systems that follow this model include the Government
Securities Clearing Corporation, the
National Securities Clearing Corporation,
the Depository Trust Company, and the
Participants Trust Company in the United

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30

NOVEMBER/DECEMBER 1997

involved ten banks and four currencies,
each of the ten banks would be informed
at the end of the day of its net position
(debit or credit), in each of the four
currencies, vis-a-vis the other members.
Settlement of each bank’s obligation in
each currency would then proceed in separate national large-value payments systems
(such as CHIPS or Fedwire in the United
States) and therefore would not be linked
or simultaneous.
A multilateral foreign-exchange
netting agreement that nets currency-bycurrency reduces overall settlement risk by
lowering the amount of each currency
owed to or by any given bank. That is, all
of a bank’s trades involving U.S. dollars
covered by the agreement are reduced to
a smaller net U.S. dollar amount due or
receivable, all of the bank’s trades involving
deutsche marks are netted to a smaller net
deutsche mark amount due or receivable,
etc. As in the “Model 3” DVP systems for
securities settlement, one could think of
this foreign-exchange netting approach as
parallel provisional settlement rather than
linked final settlement (“Model 1”). Final
settlement of the net positions in each currency must still be carried out separately.
Both multilateral and bilateral foreignexchange netting arrangements currently
exist in the United States and Europe.
The Multinet International Bank and
ECHO (Exchange Clearing House) are
multilateral foreign-exchange netting
services, while FXNET, S.W.I.F.T. (Society
for Worldwide Interbank Financial
Telecommunication), and VALUNET
provide bilateral netting services for banks
engaged in foreign-exchange trading
(Bank for International Settlements,
1996, p. 15-16).
A newly formed venture, CLS Services,
Ltd., is an important private-sector initiative that will attempt to implement a PVP
system that is analogous in some respects
to a “Model 1” DVP system but is closer to
traditional correspondent banking in other
respects. CLS Services, Ltd., which is
jointly owned by a group of banks active
in foreign-exchange trading, plans to create
a subsidiary bank to function as a multi-

currency financial institution that can
perform simultaneous, matched, “on-us”
transfers of various currencies. Formed
by the so-called G-20 group of major international banks, this “continuous linked
settlement” bank (hence CLS Services)
would eliminate the time delay between
settlement of the two legs of a foreignexchange transaction, which is the source
of the most serious risk exposures in this
market (Bank for International Settlements,
1996, p. 22; “Global Banks,” 1997). In
addition to being available for direct use
by individual trading banks, the CLS bank
could also assist netting arrangements
such as FXNET and Multinet in discharging
the net obligations that remained among
participants after netting in individual currencies had occurred.
As in a “Model 1” PVP system, the
CLS bank could provide virtually instantaneous confirmations to trading banks in
each currency on a gross basis. Settlement
would be final, although withdrawals of
individual currencies might be delayed by
the failure of counterparty banks to fund
their CLS accounts with the appropriate
mix of currencies. Thus, the CLS bank
could not provide unconditional protection
against foreign-exchange liquidity risk—
as with a true “Model 1” PVP system—
because some trades that are settled may
not allow counterparties to withdraw specific currencies immediately (Bank for
International Settlements, 1996, p. 22).
For example, suppose a Japanese
bank and an American bank use the CLS
bank for a single foreign exchange trade
during one day. The Japanese bank
promises to send an American bank yen,
and the American bank promises to pay
dollars to the Japanese bank. If a sufficient
amount of yen is not in its CLS account
by the end of the day, the Japanese bank
must fund that account with an RTGS
transfer to the CLS bank over BOJ-Net.
If the Japanese bank is declared insolvent
before funding its yen account, the CLS
bank might cancel this trade.9 In any
case, if the American bank has been
counting on the trade for fulfilling other
time-critical obligations, the failure of

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31

9

This would occur if the CLS
bank operated according to the
“guaranteed refund system”
(Bank for International
Settlements, 1996, p. 22).
Alternatively, if the CLS bank
operated under the “guaranteed receipt system,” a member bank could receive yen if
and only if it delivered dollars
to the CLS bank. The latter system would involve a performance guarantee from the CLS
bank requiring capital or losssharing commitments by owner
banks. At present, it appears
likely that the CLS bank will
take the latter approach.

NOVEMBER/DECEMBER 1997

its counterparty could create disruptions
or delay.
Hence, although the CLS bank could
eliminate the settlement and liquidity risks
inherent in a large number of foreignexchange trades, it would not be operating
as a true “Model 1” PVP system. Instead,
the CLS bank would be acting as a correspondent bank for each of its account
holders. Thus, it could not unconditionally guarantee that all foreign-exchange
trades would be perfectly liquid. Only
central banks with the ability to create
unlimited amounts of their own currency
can jointly operate a “Model 1” PVP system.

In addition to creating risky private
intraday credit, however, netting arrangements also reduce the absolute amount of
settlement activity that must ultimately
occur. In general, the longer the period
between successive settlements, the greater
the reduction in settlement obligations.
Consequently, the direct credit exposures
that build up over an extended clearing
cycle in a netting system are, at the same
time, reduced by the process of netting
(Board of Governors of the Federal
Reserve System, 1988, p. 4). The greater is
the amount of two-way trading among a
set of counterparties during a clearing
cycle (i.e., buying and selling of the same
financial instrument), the more likely it is
that the risk-reducing aspect of netting
will outweigh the risk-increasing nature of
deferred settlement. Thus, when bilateral
and multilateral netting arrangements are
soundly structured in appropriate circumstances, regulators generally welcome
them. This conclusion is particularly true
today after the decade-long efforts by the
Committee on Payments and Settlement
Systems of the Bank for International Settlements, and by individual central banks,
to upgrade the soundness of existing and
proposed multilateral net settlement
systems in the G-10 countries.

NET SETTLEMENT SYSTEMS

10

It is important to note that RTGS
and DNS systems are not direct
competitors in all respects.
DNS systems typically rely on
the national RTGS system for
final daily settlement of the
multilateral net (or “Net Net”)
obligations incurred by participants in the netting system.

11

In correspondent banking, one
bank holds deposit balances at
another (or they hold balances
with each other) that can be
debited or credited for funds
transfers, foreign exchange,
securities, derivatives, or other
transactions. Accumulated net
credits or debits may be settled
periodically through transfers of
central bank reserves. These
relationships are very important
in foreign-exchange trading
because they form the only link
between different national
RTGS or DNS systems. For an
overview of payments and settlement in the foreign exchange
market, see Gilbert (1992).
For detailed discussions of market practices and risks in the
foreign-exchange market,
see Bank for International
Settlements (1989, 1990,
1993a, 1996).

The principal alternatives to RTGS
systems for large-value funds transfers
are bilateral correspondent-banking
relationships and multilateral deferred net
settlement (DNS) systems.10 Both
correspondent banking and multilateral
netting systems offset gross payments
obligations in order to arrive at a much
smaller net settlement obligation.11
Similarly, the principal alternative to tradeby-trade gross settlement of trades in
securities and other financial obligations
is a net securities or financial obligation
settlement system.
The primary benefit to financial institutions of netting is a reduced need for
immediate liquidity or ownership of securities, since final settlement is deferred
until the end of the clearing cycle (usually
the end of the business day). Because
deferral of settlement implicitly requires a
financial institution to extend credit to
another institution from which it expects
to receive funds or securities, longer elapsed
periods between settlements also imply
greater exposure of individual payee banks
to the credit risks posed by their payments
counterparties, the payors (Shen, 1997,
pp. 48-50). Thus, it is clear that both the
primary benefits and the principal costs of
netting derive from the use of private
credit in settlement activity (see shaded
insert: “Private Credit Extensions in Net
Settlement Systems”).

Deferred Net Settlement (DNS)
Large-Value Funds Transfer Systems
Table 3 lists the major DNS large-value
funds transfer systems in the G-10 countries.
The largest private DNS payments system in
the world is the Clearing House Interbank
Payments System (CHIPS) in the United
States (see Tables 4 and 5). CHIPS is
operated by the New York Clearing House
Association and includes over 100 domestic
and foreign banks as its members. Fedwire,
by way of contrast, connects roughly
10,000 financial institutions in the United
States to the Federal Reserve and thereby
to each other.
Private-sector large-value payments
clearing houses like CHIPS are not a
prominent feature of the Japanese or most
European payments systems, for largely

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NOVEMBER/DECEMBER 1997

PRIVATE CREDIT EXTENSIONS IN
NET SETTLEMENT SYSTEMS
Deferring final settlement requires an implicit extension of credit from payee
to payor in net settlement systems. Intraday loans are economically important even
though there is no explicit private intraday credit market in most countries. That is,
the time value of an intraday loan may be zero (more precisely, no market price exists),
but net settlement systems require them in order to function, and lenders in such
systems bear the sometimes substantial credit risks imposed by borrowers.
Consider an example in which three banks, called Banks A, B, and C, are the members of a DNS system. They enter payment orders at different times during the business day, which extends from 9:00 a.m. to 5:00 p.m. (the time of each payment request
is listed in parentheses in the table below). Final settlement occurs at 5:00 on a multilateral net basis. All three banks know the amount and timing of upcoming payment
flows at the beginning of the business day:

Paying Bank (Borrower)
Bank A
Bank B
Bank C

Receiving Bank (Lender)
Bank B
Bank C
$100 (9:05 a.m.)
$100 (4:59 p.m.)
$100 (9:10 a.m.)
Bank A

Final settlement occurs at 5:00 p.m., so Bank B is effectively lending Bank A $100
during the period from 9:05 a.m. to 5:00 p.m. Typically there is no explicit cost for this
intraday credit, although collateral requirements may impose some opportunity cost on
the payor. Similarly, Bank C receives an intraday loan of $100 from Bank A between
9:10 a.m. and 5:00 p.m., while Bank B receives an intraday loan of $100 from Bank C
between 4:59 p.m. and 5:00 p.m. Viewed at the end of the day, all banks are in an identical position with zero net debits or credits vis-à-vis the system. In other words, no
actual payments are required to settle the day’s transactions if the netting agreement has
legal standing (see the section on the legal status of netting agreements below) or if all
banks are solvent at the end of the day.
This end-of-day symmetry masks the fact that Bank B was a net lender to Bank C
for most of the business day, whereas it received a promise for $100 from Bank A early
in the day. If Bank B had not entered its payment order to Bank C before 5:00 p.m.,
and had Bank C been unable to settle its resulting net debit of $100, Bank B would be
forced to recover its $100 claim against the netting system. Bank C would have a net
debit position in the system of $100, so Bank B would have a claim against Bank C and
any other resources already committed to support the netting system, such as collateral,
capital, or back-up lines of credit underwritten by surviving members. Thus, relatively
modest end-of-day net settlement amounts in a multilateral netting system (zero in this
example) may disguise substantial intraday credit exposures faced by individual banks.
historical reasons. There are a few exceptions, however. The United Kingdom’s
Clearing House Automated Payment System
(CHAPS) settled large-value funds transfers
in pounds sterling on a multilateral net
basis between 1984 and 1996. Subse-

quently, the system became a real-time
gross settlement interface between private
banks and the Bank of England (European
Monetary Institute, 1996, p. 627).
Another private DNS payments system
in Europe is the ECU Clearing and

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NOVEMBER/DECEMBER 1997

Table 3

Deferred Net Settlement (DNS) Systems in G-10 Countries
Country
Belgium
Canada
France
Germany
Italy
Japan
Netherlands
Sweden
Switzerland
United Kingdom
United States
Cross-border

Name of DNS System
CH (Clearing House of Belgium)
LVTS (Large Value Transfer System)
SNP (Système Net Protégé)
EAF2 (Elektronische Abwicklung Frankfurt)
ME (Electronic memoranda); SIPS
Zengin (Zengin Data Telecommunications System);
FEYCS (Foreign Exchange Yen Clearing System); BOJ-Net
8007-SWIFT (Society for Worldwide Interbank
Financial Transfers); FA
—
—
CHAPS (Clearing House Automated Payment System)
CHIPS (Clearing House Interbank Payments System)
ECU Clearing System

Year of Implementation
NA
1998
1997
1996
1989, 1989
1973, 1989, 1988
1982, 1985
—
—
1984
1970
1983

SOURCES: Bank for International Settlements (1997a), p. 4 and Annex 2; Bank for International Settlements (1995b), Table 10a; Bank
for International Settlements (1993b), pp. 305-06; pp. 498-501.

12

The Lamfalussy standards specify
the minimum criteria any private net settlement system
must meet to be acceptable to
a G-10 central bank (Bank for
International Settlement,
1990). See the discussion and
Table 6 for an overview of
these standards.

Settlement System operated by the ECU
Banking Association (EBA) since 1983
(European Monetary Institute, 1996,
pp. 692-96). After clearing interbank payment obligations denominated in ECU
(the European Currency Unit, a fixed
basket of European currencies) on a multilateral net basis, final settlement takes
place in accounts at the Bank for
International Settlements (BIS) in Basle,
Switzerland. The BIS acts as a correspondent bank for all the clearing banks in
the arrangement, who agree to maintain
clearing accounts without overdraft
features. The private ECU clearing system
does not currently meet the Lamfalussy
minimum standards for netting schemes.12
However, reforms including sender caps,
liquidity-sharing, and loss-sharing
agreements are being implemented to
increase the safety of settlement procedures
(European Monetary Institute, 1996,
p. 695). The EBA plans to convert the
ECU clearing system to one that will settle
interbank euro payments on a multilateral
net basis beginning in 1999, or whenever
the single currency is introduced. The

new system will be called EURO 1 and is
expected to play a role in Europe analogous
to that of CHIPS in the United States,
providing large-value multilateral net settlement services alongside TARGET, the
RTGS system for euro.
Finally, the major banking groups in
many European countries (for example,
private commercial banks, state-owned
savings banks, or co-operative banks)
either clear interbank payments through
the national central bank or operate
their own centralized correspondent
(or bankers’ banks) that provide clearing
and other services to the sector’s members
(see Bank for International Settlements,
1993b, pp. 166-78, for a discussion of
the German case). These private-sector
arrangements handle primarily small-value
transactions, however.
In Japan, the Foreign Exchange Yen
Clearing System (FEYCS) was established
in 1980 by the Tokyo Bankers Association
(TBA) to handle yen settlement of crossborder transactions, comparable to the role
of CHIPS in the United States. However,
the TBA transferred the operation of FEYCS

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NOVEMBER/DECEMBER 1997

Table 4

Milestones in the Development of CHIPS (Clearing House Interbank
Payments System)
Event

Date

Computerization of message transfers for participants

April 1970

Paper Exchange Payment System (PEPS) implemented for non-members

March 1972

Larger computer installed; all PEPS-using banks become CHIPS participants

October 1974

Same-day settlement implemented through special reserve account at
the Federal Reserve Bank of New York

October 1981

Bilateral credit mechanism implemented

October 1984

Sender net debit caps installed

March 1986

Loss-sharing arrangement among all participants implemented; collateral
requirements to support each participant’s contingent liability

October 1990

New criteria for settling participants adopted

March 1992

New message format adopted

August 1992

Settlement-finality improvements announced, including reduced net debit caps,
increased collateral requirements, and modified loss-sharing procedures

July 1995

SOURCES: Hook (1994); Richards (1995).

to the Bank of Japan in 1989 (Bank for
International Settlements, 1993b,
pp. 261-68).

essentially, those that meet the Lamfalussy
standards — and all other DNS systems
(see Table 6). A secured DNS system is
one that is capable of settling all net obligations at the end of a clearing cycle, even
when the member with the largest net-debit
position is unable to settle (Standard 4).
Banks may establish a “failsafe” settlement
guarantee by posting collateral in advance,
lodging capital funds at the clearing house,
forming a joint back-up settlement agreement with the members, obtaining a
government guarantee, or some combination
of these elements (Bank for International
Settlements, 1997a, pp. 39-42).
In principle, direct monitoring by banks
of other banks is a potentially significant
benefit associated with private multilateral
net settlement systems. This is because
private financial institutions may obtain
finer levels of detail, in a more timely
fashion, about other market participants
than is possible for central banks or other
banking supervisors. The financial exposure one bank creates for another in such a
system provides a strong incentive for the
creditor bank to monitor the debtor bank.

Monitoring and Risk Management
in Deferred Net Settlement Systems
The BIS and individual central banks
have strongly encouraged the members of
many DNS systems to intensify their riskmanagement efforts in recent years, hence
increasing the private-sector costs of using
them. In Europe, the central banks of the
(then) European Economic Community
set down recommendations regarding the
minimum common features of domestic
payments systems in 1993 (Bank for International Settlements, 1997a, p. 40). In the
United States, the Federal Reserve updated
its payments-system risk policy for the
design and operation of privately operated
large-value multilateral netting schemes in
1994 and again in 1997 (Bank for International Settlements, 1997a, p. 40; McConnell,
1997, p. 2).
Many G-10 central banks now distinguish between “secured” DNS systems —

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Table 5

Participants, Number, and Value of Transfers on CHIPS
Year

Year-End Number
of Participants

Annual Number
of Transfers (millions)

1971
1975
1980
1985
1990
1991
1992
1993
1994
1995
1996

15
63
100
142
131
126
122
121
115
111
104

0.8
6.0
13.2
24.9
37.3
37.6
39.1
42.2
45.6
51.0
53.4

Annual Dollar Value
of Transfers (trillions)
1
11
37
78
222
217
238
266
295
310
332

SOURCE: CHIPS (1997).

Reliance on direct bilateral monitoring
by banks in multilateral payments and settlement systems is subject to at least two
shortcomings, however. Most importantly,
decentralized monitoring in multilateral
DNS systems suffers from a fundamental
“free-rider problem.” Each participating
bank realizes that any losses created by a
member in excess of its own resources
will be shared among all the remaining
members in some fashion. Any individual
bank’s financial exposure to a counterparty
in the system is therefore attenuated by
the co-insurance feature of the multilateral
system; consequently, that bank’s incentive
to monitor is reduced. Some elements
of shared financial responsibility remain
even in systems that attempt to allocate
residual risks to members in proportion
to their dealings with the defaulting
participant. Therefore, diffused financial
risks in a multilateral payments and
settlement system necessarily imply a
reduced intensity and quality of
monitoring relative to purely bilateral relationships.
An additional shortcoming is that
the monitoring efficiency of a net settlement
system is likely to be sensitive to the size
of the membership. Increasing the number
of participating banks increases the monitoring burden on each bank, a situation
that may lead to a decreased quality of
monitoring. This problem exists indepen-

dently of the free-rider problem identified
above; in fact, it becomes more acute the
less the system shares risk among all participants. To see this, consider a multilateral
DNS system that provides no risk sharing
at all among its surviving members and
which “unwinds” (cancels) all transactions
involving a defaulting bank. Each member
is fully exposed to the losses created by
each of its transactions with a failed counterparty, so it must monitor all of them as
if no multilateral system existed at all. If
the increased monitoring burden results in
a lower quality of monitoring, the end result
is a greater risk of unanticipated disruption
(Bank for International Settlements, 1997a,
pp. 39-43). Restricting the membership
of any payments or settlement system to
encourage better monitoring incentives is
likely to run afoul of antitrust regulations,
however. This conflict between restricting
access in order to preserve monitoring
incentives and the need to remain open
to new members to promote competition
is likely to become more serious as financial markets become more global and
interconnected.
Centralized monitoring (i.e., delegation
of monitoring responsibilities to a central
authority, such as a clearing house) may be
a viable option in some cases, but centralization entails difficult issues in its own
right. These include the need to “monitor
the monitor” and to decide on a formula

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Table 6

The Lamfalussy Standards
Area of Concern

Proposed Standard

1. Legal basis of netting schemes

Netting schemes should have a well-founded legal basis under all relevant jurisdictions.

2. Participants’ understanding
of financial risks

Netting scheme participants should have a clear understanding of the impact of the particular scheme on each of the financial risks affected by the netting process.

3. Credit- and liquidity-risk
management procedures

Multilateral netting systems should have clearly defined procedures for the management
of credit risks and liquidity risks that specify the respective responsibilities of the netting
provider and the participants. These procedures should also ensure that all parties have
both the incentives and the capabilities to manage and contain each of the risks they
bear and that limits are placed on the maximum level of credit exposure that can be
produced by each participant.

4. Settlement capability

Multilateral netting systems should, at a minimum, be capable of ensuring the timely
completion of daily settlements in the event of an inability to settle by the participant
with the largest single net-debit position.

5. Admission criteria

Multilateral netting systems should have objective and publicly disclosed criteria for
admission that permit fair and open access.

6. Operational reliability

All netting schemes should ensure the operational reliability of technical systems and the
availability of back-up facilities capable of completing daily processing requirements.

SOURCE: Bank for International Settlements (1990, p. 5).

for allocating any losses that cannot be
covered by the pool of resources (collateral
or equity capital) held at the central institution. The centralized monitor must also
guard against exposing members to moral
hazard: When freed from the direct scrutiny
of other members, they might be more
tempted to act in ways that would increase
the system’s overall risk. In sum, privatesector risk management in net settlement
systems is promising but problematic.
Participating banks possess some natural
advantages over regulators in providing
monitoring services, but private monitoring
is likely to be costly and imperfect, regardless of how it is done.

are similar for multilateral payments and
obligation netting systems. In particular,
each member of the netting arrangement
enters trades with counterparties, which
are recorded in real time. It then settles its
final obligation to the system—either a net
credit or a net debit—only at the end of
the clearing cycle. Settlement occurs either
several times during the day or once at the
end of the day.
In addition to providing periodic net
settlement of financial obligations, organized
derivatives exchanges also require firms to
post and maintain margins. That is, members must make available to the clearing
house cash or other liquid assets sufficient
to cover likely changes in the net value of
the firm’s positions implied by movements
in financial markets (Bank for International
Settlements, 1997b, pp. 21-4). Margin
management is an important risk-control
tool of derivatives exchanges that requires
efficient banking operations (so-called
“money settlement”) to function effectively.
In this sense, financial-obligation netting
systems can be compared to DVP systems
for securities settlement.

Financial-Obligation
Netting Systems
Table 7 lists some important netting
systems and agreements for securities,
derivatives, and foreign exchange. These
arrangements are collectively known as
financial-obligation netting arrangements
(in contrast to payments netting arrangements). The basic mechanics of netting

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Table 7

Private-Sector Financial-Obligation Netting Arrangements
Netting System or Agreement

Organizers

n

Descriptio

Futures and options exchanges (Chicago
Board of Trade, Chicago Mercantile Exchange,
Chicago Board Options Exchange, London
International Financial Futures Exchange,
Deutsche Terminboerse, Marché à Terme
International de France, etc.)

Member firms

Clearing Corporations are central counterparties for all transactions with end-of-day
mark-to-market and daily settlement
through a single settlement account across
all exchange-traded contracts.

Government Securities Clearing
Corporation (GSCC)

Member firms

Government securities clearing agency
offering automated trade comparison, netting, and risk-management services

National Securities Clearing
Corporation (NSCC)

Member firms

Clearing agency for U.S. equities, long- and
medium-term corporate and municipal
bonds, guarantees settlement

Model Interbank Foreign Exchange
Netting Agreement (IFEMA)

Foreign Exchange Committee
(FEC) and the British Bankers
Association (BBA)

Master agreement for bilateral close-out
netting, netting by novation, or payment
netting of foreign-exchange obligations in
an over-the-counter (off-exchange) setting

International Securities Dealers Association
(ISDA) obligation-netting agreements

ISDA

Framework agreements for implementing
obligation netting in an over-the-counter
setting

Foreign Exchange and Options Master
Agreement (FEOMA)

FEC and BBA

Master agreement for bilateral close-out
netting of over-the-counter options

SOURCES: Richards (1995, p. 1075); Labrecque (1996, p. 21); Foreign Exchange Committee (1995); Federal Reserve System (1997).

Organized derivatives exchanges use
two primary models for carrying out trading
activities and settling margins: the central
bank model and the private settlement
bank model (Bank for International Settlements, 1997b, pp. 12-15). In the central
bank model, the derivatives exchange
and/or its members hold reserve accounts
directly at the central bank; these accounts
can be debited and credited in real time
to effect cash transfers in support of trading
and margining activities. Most continental
European G-10 derivatives exchanges
follow this model (Bank for International
Settlements, 1997b, Annex 2). This
money-settlement model for derivatives
exchanges is similar to the “Model 2” DVP
securities settlement system noted above
(gross securities settlement followed by
deferred net settlement of payments),
except that the roles of the two financial
instruments are reversed. That is, in the

central bank model of money settlement
for derivatives exchanges, the traded financial obligations are settled on a deferred
net basis, while cash payments—including
margins—are carried out in the national
RTGS system. Of course, cash payments
to effect margin and open-position adjustments are not carried out in real time on
any derivatives exchange; the point is that
the central bank model of money settlement
can provide continuous marking-to-market
in the cash account.
The second type of money-settlement
model for derivatives exchanges involves
private settlement banks. This approach—
which is used in the United States, the
United Kingdom, the Netherlands, and
Japan (Bank for International Settlements,
1997b, Annex 2)—is comparable to a
“Model 3” DVP system in which both
financial obligations and funds transfers
are settled on a net basis some time after

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be transmitted to others through the payments system, because settlements are
never conditional on a paying institution’s
solvency.14
By way of contrast, DNS systems
merely accumulate records of “IOUs”—
that is, credit extensions—issued by banks
to each other during the business day.
These IOUs are netted against each other
and settled in cash or by delivery of the
relevant securities or foreign currency at
the end of the clearing cycle—as long as
none of the participants in a net debit position defaults. In case of such a default, the
entire set of transactions may be unwound
and re-entered after all of the defaulting
institution’s transactions have been deleted.
Another factor that makes settlement
lag a source of risk is that a party due to
receive a payment may prematurely consider
the payment final. Since the payee may
receive some information about the pending
payment during the settlement lag, either
from the payor directly or from the DNS
system, there may be a temptation, or
indeed an established local business practice, to take the payment for granted.
Clearly, decoupling the payment information from the final settlement increases
DNS flexibility and the flexibility of financial institutions that use it, but it also
creates the potential for complex scenarios
of disruption.15

the trades (or margin calls) are issued.
Continuous marking-to-market in central
bank funds is not feasible in this model
unless private settlement banks are prepared
to give derivatives exchange members
essentially direct access to RTGS systems
via deposit accounts. This approach
would approximate the indirect access to
Fedwire that non-bank securities dealers
obtain in the United States via their
clearing banks.

Systemic Risk in Gross and Net
Settlement Systems
If settlement lag is the “building block”
of risk in the DNS systems, then systemic
risk represents the collapse of the edifice.
Systemic risk in its narrowest sense refers to
the possibility of a chain reaction of settlement failures in an interlinked payments or
settlement system. In more general terms,
systemic risk encompasses situations in
which the credit or liquidity problems for
one or more market participants create
substantial credit or liquidity problems
for participants elsewhere in the financial
system (Berger, Hancock, and Marquardt,
1996, p. 706). It is largely the specter
of systemic risk that has preoccupied
payments-systems experts at G-10 central
banks for the last decade or so, generating
the veritable cascade of BIS reports
mentioned earlier. The most noteworthy
of these reports may have been the
Lamfalussy Report of 1990. Under these
standards, so-called secured DNS systems
are characterized by specific riskmanagement provisions their members
must have implemented to reduce
systemic risk (Table 6).
Gross settlement systems seek to eliminate systemic risk by inserting “circuit
breakers” into payments and settlement
chains. The key is that an institution
attempting to make a payment or effect a
settlement over a gross settlement system
is required to post “cash in advance” (or
collateral or securities, depending on the
type of payments or settlement system).13
Gross settlement systems do not allow the
insolvency of one financial institution to

13

Fedwire allows payors to overdraw their reserve accounts
without posting specific collateral; for this reason, some have
compared it to a DNS system
(Kahn and Roberds, 1996,
p. 3). From the payee’s (and
the systemic-risk) perspective,
however, Fedwire functions as
an RTGS system, since every
payment is final when received.

14

Unfortunately, in a gross settlement system a distressed financial institution’s illiquidity can
be transmitted to other participants, resulting in a systemic
liquidity crisis or gridlock.

15

Some RTGS systems are also
vulnerable to this problem, as
noted above. In particular,
those that place outgoing but
unexecuted payment orders in
a queue and allow receiving
banks to “look into the queue”
may encourage banks to anticipate payments before they
become final.

Legal Status of Netting
Arrangements
As noted, the ultimate source of risk
in DNS systems is settlement lag, the time
that elapses between the inital transmission
of a payment request from the sending
bank to the DNS system and the final
receipt of good funds or securities by the
receiving bank (Bank for International Settlements, 1997a, pp. 7-9; Shen, 1997,
pp. 48-53). In addition, legal uncertainty
surrounding netting agreements in many
jurisdictions makes the risk of settlement
failure costly. Rather than being exposed
to the smaller risk that a valid netting
agreement implies, some banks could lose
the larger gross amount due from a coun-

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NOVEMBER/DECEMBER 1997

16

This section follows discussions
in Russo (1996, pp. 97-100),
Cohen and Wiseman (1994,
pp. 53-9), and Patrikis, Bhala,
and Fois (1994, pp. 36-7).

terparty if the netting agreement did not
stand up under legal challenge.
The legal status of interbank netting
agreements in the United States results
from four different bodies of insolvency
law: (1) the U.S. bankruptcy code as
amended in 1990, (2) the Financial Institutions Reform, Recovery, and Enforcement
Act of 1989 (FIRREA), (3) New York State
banking legislation (including Article 4A
of the Uniform Commercial Code applying
to wholesale electronic funds transfers,
drafted in 1989), and (4) the Federal Deposit
Insurance Corporation Improvement Act of
1991 (FDICIA).16
In general in the United States, netting
or set-off arrangements are stayed (i.e., not
allowed to proceed) in bankruptcy because
netting imposes a de facto seniority structure where none exists de jure (Rochet and
Tirole, 1996, p. 837). Similarly, secured
creditors are prevented from repossessing
their security after a Chapter 11 bankruptcy
filing (Sharpe and Nguyen, 1995, p. 275).
From this perspective, netting is inconsistent with the overall intent of formal
bankruptcy proceedings, which is to
satisfy all creditor demands in an orderly
and equitable manner according to strict
priority rules. However, certain financial
contracts are exempted from this general
disallowance of netting in bankruptcy
because Congress and many state
legislatures have accepted the argument
that some netting arrangements should be
allowed to proceed to avoid triggering systemic failures in the financial system.
The bankruptcy code, FIRREA, and
New York State banking legislation
(including Article 4A) are similar with
respect to netting agreements, including
“safe harbors” for swap agreements, securities contracts, commodities contracts,
forward contracts, and repurchase
agreements (Russo, 1996, p. 98). In addition, Article 4A and the Federal Reserve’s
Regulation J (governing large-value
electronic funds transfers, including FedWire) explicitly permit net settlement of
wholesale electronic funds transfers among
banks in order to encourage banks to
accept payment orders from financially

weak sending banks (Patrikis, Bhala, and
Fois, 1994, pp. 36-7).
In contrast to the previous case-bycase legal approach to netting, the FDICIA
of 1991 essentially permits all netting
agreements among financial institutions
(including clearing houses) to proceed
notwithstanding bankruptcy or insolvency.
In other words, netting agreements now
have a firm legal foundation in U.S. law
with the implication that the de facto
seniority created by a netting agreement
among financial institutions has become
de jure and enforceable in transactions
for which U.S. law prevails.
In general, the legal status of netting
agreements in countries outside the
United States is uncertain (Bergsten, 1994,
pp. 451-52; Padoa-Schioppa, 1994,
pp. 30-5; Bureau of National Affairs, 1997,
pp. 721-22). The United Nations
Commission on International Trade
(UNCITRAL) adopted the UNCITRAL
Model Law on International Credit Transfers in May 1992, which could have given
an impetus to international efforts to
solidify the legal standing of netting. The
model law was developed under the influence of the drafters of Article 4A of the
U.S. Uniform Commercial Code, which
gives explicit legal standing to netting of
wholesale electronic funds transfers. In
the end, however, the UNCITRAL model
law did not include any provisions on the
applicability of netting agreements. Consequently, there are still gaps in most
countries’ insolvency laws concerning
the enforceability of netting agreements
(Bergsten, 1994, p. 452).
In Switzerland, provisional payment
orders issued by a bank that is subsequently
declared bankrupt are deemed revoked
(the “zero-hour rule”)—that is, any
netting agreement including such a bank
must be unwound. This rule makes
netting agreements unreliable for the participants (Hess, 1994, p. 334). Zero-hour
rules exist in other countries, as well.
In Japan, settlement of net positions
in FEYCS, the Foreign Exchange Yen
Clearing System, is not explicitly insured
by the Bank of Japan. A loss-sharing

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NOVEMBER/DECEMBER 1997

arrangement is in place, but beyond this,
there is uncertainty about the implications
for participants of the default of a member
(Saito, 1994, pp. 223-24). The Bank
of Japan’s implicit guarantee of settlement
is widely assumed, however.
In the United Kingdom, finality of
payment is determined by common law.
In the absence of clear precedents in various
kinds of netting arrangements, the legal
status of the participants in any given
agreement is not entirely clear (Beaves,
1994, p. 364).
In France, the wholesale payments
netting system, SAGITTAIRE, is based
on S.W.I.F.T. (Society for Worldwide Interbank Financial Telecommunication)
messages, which are irrevocable from the
point of view of the sender once they have
been received. However, the Banque de
France may revoke some exchanges. The
net positions of members are drawn up
after the close of the accounting day, then
debited or credited on the books of the
Banque de France. Transactions do not
become final until 10:00 a.m. the following
day. Hence, the legal status of participants
in SAGITTAIRE in the face of a default of
a participant is unclear (Perdrix, 1994,
pp. 148-49). Recently, however, changes
have been made in the legal system to
assure the legal enforceability of bilateral
netting agreements (Padoa-Schioppa,
1994, p. 33).
In the European Union as a whole,
only four countries (Belgium, Germany,
France, and Italy) currently provide legal
assurance for the enforceability of bilateral
netting, while multilateral netting
agreements have no firm legal standing in
any E.U. country.17 Some progress toward
recognition of netting agreements for bank
supervisory purposes has been achieved
recently at the E.U. level. In particular, the
so-called “EC Netting Directive” was issued
by the European Parliament and the Council
of the European Union in March 1996
(Deutsche Bundesbank, 1996, pp. 146-47).
This allows national bank supervisors to
adjust banks’ capital requirements downward
on the basis of close-out netting agreements
covering over-the-counter derivative

instruments if the concerned banks obtain
legal opinions stating that the agreements
are likely to be legally binding in all
relevant jurisdictions. This is, of course,
far short of the solid legal basis for netting
agreements available in the United States
since 1991 and required by a strict interpretation of the Lamfalussy Standards.

CONCLUSION
The wholesale payments and settlement
systems of G-10 countries have undergone
significant change in recent years. Recognizing the inherent limitations and
vulnerability to disruption of bilateral and
multilateral netting arrangements for payments and settlement, private financial
institutions and central banks alike have
implemented measures to make them safer.
Secured net settlement systems pose
less threat of systemic disruption to G-10
payments and settlement systems than
unsecured systems. Further progress in
establishing legal foundations for domestic
and cross-border netting arrangements
will further solidify their contributions
to the global financial system. The largest
costs associated with secured net
settlement systems today are those of
day-to-day risk management incurred
by participating banks.
A different approach to strengthening
the wholesale payments system is to create
and/or improve trade-by-trade (gross) settlement systems for large-value funds
transfers and securities settlement. Gross
settlement systems can be very effective in
reducing and isolating the sources of risk
that make netting systems vulnerable.
From the point of view of private-sector
participants, gross settlement systems
virtually eliminate the need to manage
counterparty credit risk. However, these
systems may impose significant liquidity
costs on users, or they may require
substantial risk-management measures on
the part of central banks, depending on
the design of the system.
There are several plausible hypotheses
for why the quantitative importance of gross
settlement systems remains limited at this

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41

17

Table 1 in Padoa-Schioppa
(1994, p. 32) summarizes the
legal standing of various types
of netting arrangements in the
major European Union nations
as of 1994.

NOVEMBER/DECEMBER 1997

_______. Committee on Payment and Settlement Systems, Central
Bank Payment and Settlement Services with Respect to Cross-Border
and Multi-Currency Transactions, September 1993a.

point. First, gross settlement systems have
become available in most G-10 countries
only recently. It takes time for payments
and settlement practices to change, although
they probably will continue to do so over
the next few years, in large part because central banks continue to encourage greater use
of gross settlement.
Second, existing net settlement systems
are well-established in most countries, and
they generally have a disruption-free track
record (even if they are or were unsecured).
Proponents of gross settlement systems
therefore have little actual evidence with
which to bolster their claims that net
settlement is excessively risky.
Finally, gross settlement systems may
have been adopted slowly because of their
significant liquidity costs. These costs
could be lessened if central banks paid a
market rate of interest on reserve balances
or pursued monetary policies that ensured
price stability and, hence, lower nominal
interest rates than would otherwise obtain.
Either approach might encourage financial
institutions to hold larger clearing balances
(i.e., central bank reserves in excess of legal
minimums). These balances could serve
as the basis for greater participation in
RTGS payments systems at little or no
opportunity cost. They would also provide
ready collateral for intraday securities
lending to support gross settlement of
securities transactions.

_______. Committee on Payment and Settlement Systems,
Payment Systems in the Group of Ten Countries, December 1993b.
_______. Committee on Payment and Settlement Systems, Delivery
Versus Payment in Securities Settlement Systems, September 1992.
_______. Group of Experts on Payment Systems of the Central
Banks of the G-10 Countries, Ad Hoc Committee on Interbank Netting
Schemes, Report of the Committee on Interbank Netting Schemes of
the Central Banks of the Group of Ten Countries, November 1990.
_______. Group of Experts on Payment Systems of the Central
Banks of the G-10 Countries, Report on Netting Schemes,
February 1989.
Beaves, Anthony. “United Kingdom,” Payment Systems of the World,
Robert C. Effros, ed., Oceana, 1994, pp. 343-70.
Berger, Allen N., Diana Hancock, and Jeffrey C. Marquardt. “A
Framework for Analyzing Efficiency, Risks, Costs, and Innovations in
the Payments System,” Journal of Money, Credit and Banking
(November 1996), pp. 696-732.
Bergsten, Eric E. “A Payments Law for the World: UNCITRAL Model Law
on International Credit Transfers,” Payment Systems of the World,
Robert C. Effros, ed., Oceana, 1994, pp. 407-99.
Board of Governors of the Federal Reserve System. Controlling Risk
in the Payments System: Report of the Task Force on Controlling
Payments System Risk to the Payments System Policy Committee
of the Federal Reserve System, August 1988.
Bureau of National Affairs. “Euopean Parliament Considers Directive To
Cut Risks to Interbank Payment Systems,” BNA’s Banking Report
(April 14, 1997), pp. 721-22.
CHIPS (Clearing House Interbank Payments System). Yearly Volume
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(CHIPS),” Payment Systems of the World, Robert C. Effros, ed.,
Oceana, 1994, pp. 99-128.
Humphrey, David B., Lawrence B. Pulley, and Jukka M. Vesala. “Cash,
Paper, and Electronic Payments: A Cross-Country Analysis,” Journal of
Money, Credit and Banking (November 1996), pp. 914-39.
Kahn, Charles M., and William Roberds. “Payment System Settlement
and Bank Incentives,” Working Paper 96-10, Federal Reserve Bank of
Atlanta, December 1996.
Labrecque, Thomas G. “Payments, Clearance and Settlement Systems:
The Systemic Risk Connection,” Symposium on Risk Reduction in
Payments, Clearance and Settlement Systems, Goldman, Sachs & Co.,
January 1996, pp. 19-23.
Matsushita, Yasuo. “Payment and Settlement Systems: The Current
Issues in Japan,” Speech delivered by the Governor of the Bank of
Japan, in Tokyo, February 28, 1997.
McConnell, Bill. “Fed’s Proposal Would Ease Bank Reserve
Requirements,” American Banker (November 6, 1997), p. 2.
Padoa-Schioppa, Tommaso. “Central Banking and Payment Systems in
the European Community,” International Symposium on Banking and
Payment Services, Board of Governors of the Federal Reserve System,
March 1994, pp. 27-49.
Patrikis, Ernest T., Raj K. Bhala, and Michael T. Fois. “An Overview of
United States Funds Transfer Law,” Payment Systems of the World,
Robert C. Effros, ed., Oceana, 1994, pp. 1-50.
Perdrix, Michel. “France,” Payment Systems of the World, Robert C.
Effros, ed., Oceana, 1994, pp. 127-66.
Richards, Heidi Willman. “Daylight Overdraft Fees and the Federal
Reserve’s Payment System Risk Policy,” Federal Reserve Bulletin
(December 1995), pp. 1065-77.

F E D E R A L R E S E R V E B A N K O F S T. L O U I S

43

NOVEMBER/DECEMBER

1997

John C. Robertson is an assistant professor of economics at the Australian National University and a visiting scholar at the Federal Reserve
Bank of Atlanta. Daniel L. Thornton is an assistant vice president and economist at the Federal Reserve Bank of St. Louis. Jonathan Ahlbrecht
provided research assistance.

Using Federal
Funds Futures
Rates to Predict
Federal Reserve
Actions
John C. Robertson and
Daniel L. Thornton

T

he Federal Reserve implements monetary policy by making discrete adjustments to its target for the federal funds
rate. Such adjustments are believed to have
significant implications for other short-term
interest rates, so considerable resources are
expended on forecasting the timing and
magnitude of the Fed’s next move. Many
analysts, both inside and outside of the
Federal Reserve System, look to the federal
funds futures market for an indication of
whether the market anticipates a change in
Fed policy. Because futures market participants make commitments that are contingent on what they believe the federal funds
rate will be, they necessarily look to factors
they believe will influence its course. The
Fed targets the funds rate, and the overnight
federal funds rate stays close, on average, to
the Fed’s target. Hence, the federal funds
futures rate naturally embodies the market’s
expectation of what the Fed will do.
Because of how the federal funds
futures market is structured, using the federal funds futures rate as a gauge of the market’s expectation for Fed action is trickier
than it may at first appear. The purpose of
this article is to point out the issues that
arise in using the federal funds futures rate
to forecast a change in monetary policy. In
addition, we present some evidence on the
relationships among the federal funds rate,
the federal funds futures rate, and the

federal funds target rate, and the usefulness
of the federal funds futures rate as a predictor
of whether the Fed will change its target.

THE FEDERAL FUNDS
FUTURES MARKET
The Chicago Board of Trade (CBOT)
began offering federal funds futures contracts in October 1988 (CBOT, 1992).
Unlike T-bill futures contracts, where the
contract is for the T-bill rate on a specific
day, the federal funds futures contract is
for the simple average of the daily effective
federal funds rate during the month of the
contract. The effective federal funds rate is
a weighted average of all federal funds transactions for a group of federal funds brokers
who report to the Federal Reserve Bank of
New York each day. The CBOT offers contracts ranging from the current month to
24 months out. Contracts have a nominal
value of $5 million, and their settlement
price is equal to 100 minus the average of
the effective federal funds rate for the month
of the contract. Hence, a market price of
94.3 for a one-month contract on October
15 means that the current futures rate for
November is 5.7 percent (100 – 94.3).

The Futures Rate as a Predictor of
the Average Federal Funds Rate
The futures rate is an obvious measure
of the market’s prediction for the monthly
average effective federal funds rate, after
allowing for the possibility of a non-zero
risk premium. That is,
(1)

FFFt , i = Et FFt + i + a i ,

where Et denotes the expectation conditional on all the available information up
to t; FFFt,i is the i-month ahead futures
rate; FF t + i is the average of the daily effective federal funds rate for each day of the
month; and a i is a bias term that varies
with the forecast horizon.

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NOVEMBER/DECEMBER 1997

The Futures Rate as a
Predictor of Fed Actions

Figure 1

Spread between the Fed Funds
Futures Rates and the Average Fed
Funds Rate FFFt , i - FFt + i

Because the funds rate tends to stay
reasonably close to the funds rate target on
average, it is not uncommon for analysts
to look to the federal funds futures market
for an indication of whether a change in
Fed policy is expected. However, two interrelated issues make it extremely difficult
to infer the market’s expectation for Fed
action from the behavior of the federal
funds futures rate, even after adjusting
for the underlying bias. First, the futures
rate is a forecast of the average federal funds
rate and not a forecast of the average federal
funds rate target. Second, the effect of a
target change on the average federal funds
rate depends on the timing and magnitude
of the target change. We now consider
the effect of each of these issues on the
interpretation of the federal funds
futures rate.

Percent
0.8

Two-Month Contract

0.6
0.4
0.2
0.0
–0.2

One-Month Contract

–0.4
–0.6
–0.8

0ct-88

1

Krueger and Kuttner (1996)
show that generally the federal
funds futures market efficiently
incorporates publicly available
information that is likely to
affect the direction of the funds
rate. They find, however, that
at the one-month horizon,
some variables such as inflation, industrial production
growth, etc., add significantly
to forecasts when they use the
federal funds futures rate. The
finding of non-exploited profit
opportunities appears to stem
from the use of monthly average data for the futures rate.
When the last day of the month
is used to forecast the average
funds rate in the next month,
no variable adds significantly
to federal funds futures forecast (Robertson and Thornton,
1997). Thornton (1997) has
also shown that the Fed’s practice since 1994 of changing
its funds rate target at regularly
scheduled FOMC meetings has
improved the federal funds
futures market’s forecasts of
the average funds rate.

89

90

91

92

93

94

95

96

Figure 1 presents the implied forecast
error, FFFt , i - FFt + i, for one- and two-month
contracts, where the futures rate is that of the
last day of the month for the period October
1988 through August 1997. Because the
data are measured on a monthly frequency,
the forecast errors follow MA(i-1) processes.
Also note that the variability of both series
has been somewhat lower since 1995.1
The serial correlation-adjusted estimates suggest a significant positive bias in
the federal funds futures rate forecast at
both horizons, with the bias increasing as
the forecast horizon lengthens. These estimates are consistent with the presence of a
hedging premium in the futures market. For
the one-month forecast, the bias estimate is
3.7 basis points, with a standard error of 1.3
basis points. For the two-month forecast,
the bias estimate is 7.5 basis points, with a
standard error of 3.0 basis points.
One possible explanation for the
hedging premium is that large banks, which
regularly finance a significant amount of
their loan portfolios in the spot market for
federal funds, also participate in the federal funds futures market. Such institutions may use the futures market to hedge
against increases in the spot funds rate.
If institutions that are hedging against a
potential increase in the spot rate are dominant, there could be a premium built into
the futures rates.

The Futures Rate and the
Funds Rate Target
The fact that the futures rate is not
strictly a forecast of the funds rate target
leads to an obvious identification problem.
To illustrate the problem, we express the
market’s forecast of the average funds rate
as the sum of the forecast for the average
funds rate target and the expected deviation of the average funds rate from the
average target. Substituting for the expected
average funds rate from Equation 1
then gives
(2)

FFFt , i - a i = Et FFTt + i
+ Et ( FFt + i - FFTt + i ),

where FFTt + i is the average federal funds
target rate for month t+i. The bias-adjusted
futures rate and the market’s forecast for
the average target rate will differ when the
market expects the average funds rate to
deviate from the average target. Hence,
the expected target component of the forecast cannot be deduced from the federal
funds futures rate without making
additional assumptions.

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NOVEMBER/DECEMBER 1997

Et ( FFt + i - FFTt + i ) always falls within
a certain interval. If the bias-adjusted
i-month spread between the futures rate
and the current target rate is outside this
interval, we can conclude that the market
expects a target change. While we can be
fairly certain that the market is expecting a
change in the target, we will not know the
magnitude of the change. If, on the other
hand, the bias-adjusted spread is inside this
interval, we cannot conclude that the market
is not expecting a target change. It might be
that the market is expecting a target change
that will have a relatively small effect on the
bias-adjusted futures rate.
To illustrate the implications of this
assumption, subtract the current level of
the funds rate target, FFTt , from both sides
of Equation 2 to give:

One common assumption, sufficient
to identify the market’s expectation for the
average funds rate target, is that the market
always forecasts the average funds rate to
coincide with the average target rate, i.e.,
Et ( FFt + i - FFTt + i ) = 0 . If this were true,
the bias-adjusted futures rate would be the
market’s forecast for the average funds rate
target. Since the futures rate rarely coincides
with the current target, one would conclude
that the market is almost always forecasting
a change in the target.2
We think it is unlikely that market participants always expect the average funds
rate to equal the average of the funds rate
target. The expectation for the difference
between these rates is likely to be based on
estimates of general market conditions, the
reserves positions of banks, and whether and
by how much the funds rate is permitted to
deviate from the funds rate target. For one
thing, the average funds rate has tended to
be above the average funds rate target by
about three basis points over the sample
period. That is, the average funds rate is a
biased estimate of the average funds rate
target. In addition, when the average funds
rate is above or below the funds rate target,
it tends to remain so for a few months, that
is, there is mild positive serial correlation.
Market participants likely utilize such information in developing their forecasts.

(3)

FFFt , i - FFTt - a i
= Et ( FFt + i - FFTt + i ) + Et FFTt + i - FFTt .

Assume for the moment that the market’s
forecast of how much the average funds
rate deviates from the average target is
known to always range between –20 and
+20 basis points. If the market expects no
change in the target, the bias-adjusted
spread is simply
2

(4)

(

)

FFFt , i - FFTt - a i = Et FFt + i - FFTt + i ,

and this spread will also vary between –20
and +20 basis points. If the bias-adjusted
one-month spread is outside this interval, it
must be that the market expects a change in
the target. If the spread is inside the interval,
it may or may not be the case that the market
expects a change in the target.

A Partial Identifying Assumption
Numerous other assumptions could
be made to recover the underlying market
expectations for the average of the federal
funds rate target. However, estimates of the
market’s expectation will depend on the particular identifying assumption used. Here
we consider an example of what might be
called a partial identifying assumption. It is
a partial identifying assumption because
it is sufficient only to identify some of the
occasions when the market is anticipating a
change in the funds rate target. It is insufficient for determining the magnitude of the
expected target change. Moreover, it is incapable of determining all of the occasions
when the market is expecting no change in
the target. Specifically, suppose we know that

The Expected Timing and
Magnitude of Target Changes
Over the period from October 1988 to
August 1997, there were 38 months when
the Fed changed its target for the federal
funds rate. There were 25 decreases in
the target and 13 increases. On all but
four occasions, the target change was 25,
50, or 75 basis points, with the majority
of the changes being 25 basis points.

F E D E R A L R E S E R V E B A N K O F S T. L O U I S

47

Implicitly one is assuming that
the market is always assigning
some probability, P, to a nonzero change in the target.
According to this view,
Et FFTt + i - FFTt , is equal
to P times the expectation of
a non-zero target change.
However, this interpretation
does not allow us to identify P.
For example, suppose that the
bias-adjusted spread between
the futures rate and the current
target rate is 10 basis points.
This spread is consistent with a
20 percent probability of an
expected 50 basis-point increase,
a 40 percent probability of an
expected 25 basis-point increase,
or an infinite number of alternatives. When the issue of the
timing of the change is considered, the identification problem
becomes even more severe.

NOVEMBER/DECEMBER 1997

The predicament is perhaps most
severe when the market is anticipating a
policy action late in the upcoming month.
For example, assume that futures market
participants are anticipating a 50-basispoint change in the funds rate target, from
5 percent to 5.5 percent, on the twentyseventh day of the next month, and
suppose that the bias-adjusted one-month
futures rate is 5.05 percent. Such a small
spread value could easily be mistaken to
indicate that no change in the target is
expected. Of course, if the market is predicting no action in the subsequent
month, the two-month futures rate should
be about 50 basis points higher than the
current target rate. Hence, a comparison
of the one-month and two-month
contracts would help determine whether
the market is anticipating a Fed action
next month. Even then, it would be easy
to infer that the market is anticipating a
target change two months from now,
rather than next month.

Figure 2

Spread between the Average Fed
Funds Rate and the Average Fed Funds
Rate Target
Percent
0.25
0.20
0.15
0.10
0.05
0.00
–0.05
–0.10
–0.15
Oct-88

89

90

91

92

93

94

95

96

Before August 1989, it was not uncommon
for the Fed to make two or more adjustments to its federal funds rate target in
a month.
The Fed’s adjustments to its funds rate
target affect the level of the corresponding
federal funds rate. However, the federal
funds futures market forecasts the monthly
average of the funds rate, not the funds
rate on any particular day. Consequently,
an expected target change’s effect on the
futures rate depends on when and by how
much the target is expected to change.
This problem interacts with the previously
noted identification problem. To see how,
assume that at the end of the month the
target rate is 5 percent, the bias-adjusted
federal funds futures rate is 5.13 percent,
and the average funds rate is expected to
lie within ± 20 basis points of the average
funds rate target. We might conclude
that the market is not anticipating a
change in the funds rate target. On the
other hand, it might be that the market
expects the average funds rate to equal the
average funds rate target next month. In
this case, the 13-basis-point spread is consistent with an expected rise in the target
of 25 basis points about mid-month, an
increase of 50 basis points about threequarters of the way through the month,
or even a 75-basis-point rise very late in
the month.

PREDICTING A
TARGET CHANGE
We have argued that it is extremely
difficult to extract the market’s expectation for the Fed’s funds rate target from
the behavior of the federal funds futures
rate. However, this difficulty need not
prevent us from exploring the usefulness
of the futures rate for forecasting changes
in the Fed’s target. To illustrate, reconsider the partial identifying assumption
described previously. To make this
procedure operational, we assume that
the bounds of Et ( FFt + i - FFTt + i ) in any
period are the largest and smallest values
of ( FFt - FFTt ) over the whole sample
period. This assumption is arbitrary,
but it is perhaps not too unrealistic.
As can be seen in Figure 2, the difference between the average funds rate and
the average target is often large, ranging
between about –9 and +21 basis points
over the sample period. Also, there is no
tendency for the two series to drift apart
for too long over time; consequently, the
serial correlation of the difference is only

F E D E R A L R E S E R V E B A N K O F S T. L O U I S

48

NOVEMBER/DECEMBER 1997

Figure 3

1-Month Forecasts from Fed Funds Futures Rate
(Bars represent change in average funds rate target over current target)
Percent
0.6

0.4

0.2

0.0

–0.2

–0.4

–0.6
Nov-88

89

90
Predict Change

91

92

Predict No Change

Table 1

Contingency Table for One-Month
Actual Change

Actual No Change

Predict Change

12*

6

Predict No Change

26

62

Actual Total

38

68

* One of the predicted changes was in the wrong direction.

mildly positive. That the range is asymmetric about zero stems in part from the
fact that the funds rate has tended to be
above the target level (see shaded insert
for an analysis of the sources of this bias).
Our estimate of the forecast bias in the
one-month futures rate is about four basis
points. Given our assumptions, when
the bias-adjusted spread between the
one-month futures rate and the current
target rate is outside the cut-off points,
the futures market is forecasting a target

93

94

Cut-off Points

95

96

Change in Target

change next month. A
spread that is inside the
Spread
interval may or may not
indicate an expected target
Predicted Total
change. However, for the
18
purpose of this discussion
we treat such outcomes
88
as forecasts of no target
change. This potential
106
misclassification of expectations could be a major
source of forecast error.
Figure 3 presents the
one-month bias-adjusted spread for the
period November 1988–August 1997.
The sample mean is 1.0 basis point, and
the standard deviation is 12.3 basis points.
The horizontal lines give the cutoff points,
and the vertical bars give the difference
between the current target and the average
target in the following month. Not surprisingly, the difference between the endof-month target rate and the average target
rate for the following month is almost
always less than the actual target change.

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NOVEMBER/DECEMBER 1997

Notice that the cutoff points are asymmetric about zero. The basis-adjusted
spread was less than –9 basis points on
four of the six occasions that the rule
incorrectly forecast a change, while on
the remaining two occasions it was above
22 basis points. Given our assumptions,
these are forecast errors. Conversely, the
one-month futures rate was below the
current target rate on 13 of the 26 times
that the rule incorrectly forecast no target
change, above it 11 times, and equal to it
on two occasions. Of course, we cannot
infer that these were necessarily forecasting mistakes by the market, since the
rule cannot distinguish among small
spread values.
One reason for the rule’s low accuracy
in predicting target changes is that the
futures market predicts the average level
of the funds rate. When the rule indicates
that the market is not predicting a target
change, it may actually be predicting a
target change late in the month. Hence,
when the one-month rate predicts no
change and a change occurs, it is useful
to look to the two-month federal funds
futures rate to see if the market may have
been anticipating a target change late in
the month.
Table 2 summarizes the results for
the two-month spread for the 88 occasions in Table 1 when the one-month
contract predicted no change in the target.
The bias is set at eight basis points, and
the implied interval is still –9 to 21 basis
points. As we can see, of the 26 occasions when the rule predicts no change
next month but a change occurs, a target
change is predicted at the two-month
horizon on 14 occasions. Of the 62
months when the rule correctly predicts
no change the next month, the twomonth spread predicts a change for the
following month on 16 occasions.
Table 3 presents a revised contingency table for the one-month forecast
based on the spreads for the one-month
and two-month federal funds futures rates.
Incorporating the two-month rate spread
has little effect on the overall forecast
accuracy: It declines slightly to 68 percent

Table 2

Contingency Table for Two-Month Spread
When Forecast “No Change” from
One-Month Spread

3

Predicted No
Change/Actual
Change

Predicted No
Change/Actual No
Change

Predict Change

14

16

30

Predict No Change

12

46

58

Total

26

62

88

Not surprisingly, the results are
sensitive to the cutoff values.
Basically, a narrow range increases the proportion of predictions
of a change in the target, while
a broad range leads to relatively
fewer predicted changes. The
highest overall accuracy is 73
percent, achieved using cutoff
points of –7 and +23 basis
points and ignoring the forecasted change in the wrong direction.

Predicted Total

For example, there has been a 25basis-point change on 24 occasions
since October 1988—19 decreases and 5
increases. A total of five of the decreases
and two of the increases resulted in less
than a 13-basis-point change in the
average target level. The forecast results
are summarized in a contingency table,
Table 1.
Going down the column of Table 1
headed “Actual Change,” we see that our
empirical rule correctly predicts a change
in the target on only 12 of the 38 occasions
that the target was changed and predicts
no change on the remaining 26 months.
Thus, the accuracy of this forecast is 32
percent (12/38), and on one occasion
(December 1990), the prediction was
that the Fed would raise the target when,
in fact, it was reduced. Going down the
column headed “Actual No Change,” we
see that the rule correctly predicts no
change in 62 of the 68 months when the
Fed did not change its target. Hence, the
accuracy of the no-change forecast is 91
percent (62/68). The overall accuracy is
70 percent (74/106).
While forecast accuracy is important, so is forecast reliability. The rule
only predicts that the target will change
on a total of 18 occasions. The proportion
of these forecasts that is actually correct—
the hit rate—is 67 percent (12/18). The
forecasts of no change are slightly more
reliable. The rule predicts no target
change 88 times, so the hit rate is 70
percent (62/88).3

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NOVEMBER/DECEMBER

1997

WHY IS THE FUNDS RATE A BIASED ESTIMATOR OF
THE FUNDS RATE TARGET?
The spread between the monthly average
Sources of Bias
funds rate and the average funds rate target indiFFt-FFTt
FFXt-FFTt
FFXt-FFTt
cates a bias of 3.1 basis points, with a t-ratio of
1988.10-1997.08
1994.02-1997.08
1994.02-1997.08
† That is, the monthly average funds rate has
3.7.
tended to average slightly higher than the monthly
1.43
1.53*
0.13
mean
average for the funds rate target. The standard
1.84
2.93
0.26
t-statistic
deviation of the series is 6.2 basis points, and the
variability appears to be smaller in the latter part
of the sample.
* Indicates statistical significance at the 5 percent level.
One potential source of this bias is the effect of
settlement Wednesdays. The funds rate deviates
substantially from the targeted level on the final day of the reserve maintenance period, called settlement
Wednesday. It is unusually high if reserves are scarce or unusually low if reserves are abundant. If, on
average, reserves were a little scarce on reserve settlement days, the monthly funds rate could average a
few basis points higher than the target.
It is also possible that the behavior of this series has changed over time, partly in response to the
Fed’s disclosure policy. Evidence (Thornton, 1996) indicates that, prior to the Fed’s policy of immediate
disclosure, the market took a few days to figure out that the Fed had changed its funds rate target. If so,
the funds rate would trade above the target when the Fed reduced the target and below it when the target
was raised. During the period prior to immediate disclosure, the Fed changed its funds rate target 27
times. Of these, 22 were decreases, and only 5 were increases. Hence, it would not be surprising to see a
positive bias in the funds rate over the funds rate target for this period, but the bias should disappear with
immediate disclosure.
Formerly, the Federal Open Market Committee (FOMC) announced its policy decisions about six weeks
after the previous meeting. At its February 1994 meeting, the FOMC broke this long-standing tradition and
announced the decision as soon as it was made. While the FOMC made no commitment to continue the practice, the next five changes (all increases) were announced immediately. The new policy was formalized at the
February 1995 meeting.
Evidence of the importance of the effect of settlement Wednesdays and immediate disclosure is
obtained by re-estimating the average spread. We investigate the possibility of a settlement Wednesday
effect by replacing the simple monthly average of the effective federal funds rate with a monthly average
rate that excludes settlement Wednesdays. We test the possibility that immediate disclosure could account
for the non-zero mean by estimating the average over the period from February1994 to August 1997.
The results, summarized in the table above, suggest that both of these factors have played a role. Using
data adjusted for settlement Wednesdays, we find that the average spread of the funds rate over the funds
rate target for the period from November 1988 to August 1997 was only 1.43 basis points; however, the
mean is statistically significant at the 10 percent level. Hence, while the settlement Wednesday effect plays
a role in the bias of the funds rate, it does not appear to account for it all.
The estimated mean over the period since immediate disclosure is 1.53 basis points, and the null hypothesis that the mean is zero is rejected at the 5 percent level of significance. When settlement Wednesdays are
excluded, the estimated mean drops to less than one basis point and is not statistically significant.
†

AR(1) process was used.

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NOVEMBER/DECEMBER 1997

Table 3

Revised Contingency Table For the OneMonth Horizon Based on the One- and
Two-Month Federal Funds Futures Rates
Actual Change

Actual No Change

Predicted Total

Predict Change

26*

22

48

Predict No Change

12

46

58

Actual Total

38

68

106

* One of the predicted changes was in the wrong direction.

(72/106). However, using both the onemonth and two-month spreads makes the
target change forecasts substantially more
accurate, 68 percent (26/38), but at a cost
of reduced reliability, 54 percent (26/48).
The accuracy for no-change forecasts
declines to 68 percent (46/68), while the
hit rate increases to 79 percent (46/58).
Hence, a more accurate forecast of a target
change is also associated with lower accuracy in forecasting no change.

CONCLUSIONS

4

For instance, the time series
properties of the funds rate
target itself can be utilized to
form a forecasting rule. The
fed funds futures rate may
be a useful predictor in this
context (see Robertson and
Thornton, 1997).

The federal funds futures market
naturally embodies the market’s expectation of future Fed policy. However,
the federal funds futures rate is a forecast of the average monthly level of the
funds rate. The potential for bias and
the fact that the federal funds futures
rate forecasts the funds rate and not
the funds rate target means that using
it for forecasting Fed action is considerably more difficult than it might at
first appear.
This article discusses the consequences of these difficulties for interpreting the spread of the one-monthahead futures rate over the current target
rate. In particular, we show that there is
a fundamental identification problem
that can be overcome only by making
some additional and somewhat arbitrary
assumptions. Using a particular partial
identifying assumption, we investigate

the predictive accuracy of the federal
funds futures rate over the period October
1988–August 1997. Our empirical forecasting rule correctly predicts a target
change in the following month only about
one-third of the time. The rule is much
better at forecasting no change in the
target and has an overall forecast reliability
of around 70 percent. When the two-month
federal funds futures rate is incorporated
into the analysis, the accuracy of the rule
in forecasting target changes one month in
advance is substantially improved. There
is some deterioration in forecast
reliability, however.
Because our criterion identifies only
expected changes in the target that have
a sufficiently large impact on the futures
rate, there is considerable uncertainty
about the interpretation of small deviations of the futures rate from the current
target. Consequently, the forecast errors
are not necessarily forecasting mistakes
by the market.
Because our forecasts are based
on the federal funds futures rate for
the last day of the month and the Fed
changes its target at various times during
the month, the forecast horizon is not
held constant. It is likely that the forecast accuracy will vary with the forecast
horizon. This fact is of particular interest
now because the FOMC has followed the
practice of changing its funds rate target
at regularly scheduled meetings since it
adopted the policy of immediate disclosure. Also, because meeting dates are
known in advance, the market should
not be expecting a target change in months
when there is no meeting. Although they
do not account for the inherent randomness of the federal funds futures rate nor
its bias, Pakko and Wheelock (1996) find
that the futures rate predictions improve
a few days prior to FOMC meetings. It
would be interesting to see whether there
is an optimal horizon for predicting
Fed target changes and how well the
federal funds futures rate performs
relative to other predictors of Fed
activity. These subjects are left for
other research.4

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NOVEMBER/DECEMBER 1997

REFERENCES
Chicago Board of Trade. 30-Day Interest Rate Futures:
For Short-Term Interest Rate Management, 1992.
Krueger, Joel T., and Kenneth N. Kuttner. “The Fed Funds Futures Rate as a
Predictor of Federal Reserve Policy,” Journal of Futures Markets (December
1996), pp. 865-79.
Pakko, Michael R., and David C. Wheelock. “Monetary Policy and Financial
Market Expectations: What Did They Know and When Did They Know It.?” this
Review (July/August 1996), pp. 19-32.
Robertson, John C., and Daniel L. Thornton. “Alternative Approaches
to Forecasting Fed Action,” Manuscript, Federal Reserve Bank of
St. Louis, 1997.
Thornton, Daniel L. “The Other Change in Fed Procedure,” Federal Reserve Bank
of St. Louis, Monetary Trends (July 1997).
_______. “Does the Fed’s New Policy of Immediate Disclosure Affect the
Market?,” this Review (November/December 1996), pp. 77-88.

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