The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
Remarks on Economic, Supervisory, and
Regulatory Issues Facing Foreign Banks
Operating in the United States
William J. McDonough, President, Federal Reserve Bank of New York
The views expressed in this article are those of the authors and do not necessarily reflect the position of the Federal
Reserve Bank of New York or the Federal Reserve System.
The Federal Reserve Bank of New York provides no warranty, express or implied, as to the accuracy, timeliness, completeness, merchantability, or fitness for any particular purpose of any information contained in documents produced
and provided by the Federal Reserve Bank of New York in any form or manner whatsoever.
The following remarks were given by Mr. McDonough before the
Comptroller of the Currency Conference on “Foreign Banks in the
United States: Economic, Supervisory, and Regulatory Issues” in
Washington, D.C., on July 13, 1995.
I am delighted to be here today to address this important
conference on economic, supervisory, and regulatory issues
facing foreign banks operating in the United States. I also
very much appreciate the efforts of my colleague Gene
Ludwig and his staff at the Office of the Comptroller of the
Currency in organizing these sessions. Foreign banks contribute importantly to the depth and breadth of financial
markets throughout the United States, enhancing the
sophistication and flexibility of our markets. It is a special
pleasure for me to be here because so many of your institutions are located in the Second District and have close
working relationships with us at the Federal Reserve Bank
of New York.
What I would like to do in my remarks to you this
morning is to stand back and take a look at the environment for foreign banks in the United States and comment
on some recent developments. I will also touch on some of
the challenges facing the banking industry.
I am very aware that the prospects for banks are
linked closely to the overall economic performance of the
United States. As has been widely reported, the near-term
outlook for the U.S. economy is uncertain. Particularly in
this environment, it is essential that the Federal Reserve
pursue a disciplined monetary policy, one aimed at fostering a sustained, noninflationary growth environment in
which the economy continues to shift from a higher to a
lower inflation climate. Only with price stability can productivity, real income, and living standards achieve their
highest possible levels and thereby enable both households
and businesses to function as efficiently as possible. The
key, of course, is to instill a sense of confidence that inflation is trending lower in the long term. It is the path that
in the long run creates the most hospitable environment
for businesses to grow and households to thrive.
Fostering such an environment remains the number one job of the Federal Reserve and is a key element in
maintaining the status of the United States as an attractive
market for domestic and foreign banks alike. Another very
important element contributing to an attractive climate for
banks in the United States—and especially for foreign
banks—is this country’s longstanding policy of providing
national treatment to foreign banks operating in the U.S.
markets.
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
1
What does national treatment do? Most fundamentally, national treatment accords foreign banking institutions the same rights and privileges as domestic
institutions in participating in our markets for financial
services. In practice, national treatment seeks to create a
level playing field for foreign and domestic banking insti-
Only with price stability can productivity,
real income, and living standards achieve their
highest possible levels and thereby enable both
households and businesses to function as
efficiently as possible.
tutions by giving them substantially equal access to benefit
from participating in our economy and by subjecting them
to substantially similar regulations and supervisory oversight. The national treatment policy followed by the
United States is premised on the belief that open and competitive markets strengthen all market participants and
thereby provide both cost and quality benefits to the banking institutions themselves and their customers. Our
nation feels strongly that this is the right way to achieve
fairness in the financial marketplace for all competitors,
and U.S. political leaders recently have raised the issue of
reciprocity in the policy of national treatment by others.
The principle of national treatment in banking
was reflected in bilateral treaties and later in major banking legislation enacted in the United States. It was, for
example, embodied in the Foreign Bank Supervision
Enhancement Act of 1991, which was enacted to align
supervision and regulation of foreign banks in the United
States with that applied to U.S. institutions. The strengthening of supervision and regulation of foreign banks in
1991 went hand in hand with comparable changes in legislation affecting U.S. institutions. These changes were
reflected in the Federal Deposit Insurance Corporation
Improvement Act of 1991, as well as in the earlier Finan-
2
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
cial Institutions Reform, Recovery and Enforcement Act of
1989.
Under the terms of the Foreign Bank Supervision
Enhancement Act of 1991, before a foreign bank can establish a branch or agency in the United States, the Federal
Reserve Board must determine that the foreign bank is
subject to comprehensive consolidated supervision by its
home country supervisor. While I recognize that it is not
yet the norm worldwide, I am firmly convinced that comprehensive consolidated supervision is in the best interest
of all banks if the integrity of our financial markets is to be
preserved. Maverick institutions must be precluded from
avoiding accountability to an appropriate supervisory
authority. The approval by the Basle Committee on Banking Supervision in 1992 of a statement on minimum standards endorsing comprehensive consolidated supervision of
banks worldwide provides an impetus for national regulators to move supervisory regimes in this direction.
A recent legislative effort to improve the climate
for the banking industry in the United States is the Interstate Banking and Branching Efficiency Act of 1994. This
Act substantially removes a number of barriers to full
interstate branch banking for foreign as well as domestic
banks. Interstate branching will enhance the ability of
banks to diversify their balance sheets and thereby lessen
credit risk stemming from lending concentrations.
Under the Act, bank holding companies, including foreign banks, will be able to acquire banks in another
I am firmly convinced that comprehensive
consolidated supervision is in the best interest
of all banks if the integrity of our financial
markets is to be preserved.
state beginning one year after passage of the Act, that is, by
the end of September 1995. In addition, the Act allows
branching by merger across state lines beginning June 1,
1997, provided that a state does not enact legislation prior
to this date to “opt out” of such branching arrangements.
There are also provisions allowing states to “opt in,” that
is, permit entry by merger or de novo branching before
June 1997. I applaud the demise of the outmoded restrictions on banks’ ability to do business across state lines and
believe it makes sense for all banks and their customers.
Another legislative initiative currently under discussion in the House of Representatives is the repeal of the
Glass-Steagall Act. As proposed in the Financial Services
Competitiveness Act of 1995, the repeal would, among
other things, enable both foreign and domestic banks to
expand their securities underwriting and dealing activities
through separately capitalized securities affiliates within a
“financial services holding company” structure. I not only
support the goals of this legislation but also feel its passage
is overdue.
Complementing these legislative initiatives are
efforts by federal bank supervisors to improve the supervisory environment for foreign banks. These efforts are being
directed to streamlining the supervisory process through the
implementation of the “Enhanced Framework for Supervising the U.S. Operations of Foreign Banking Organizations,”
more commonly referred to as the FBO program.
This program, which is now being put into effect,
reflects a shift in emphasis in the supervision of foreign
bank activities in the United States. Previously, the
branches and agencies of foreign banks were reviewed more
as stand-alone entities. Now, a more comprehensive
approach emphasizes the role of these entities as integral
components of the foreign banks as a whole. I am aware of
concerns that this approach seems, to some observers, to
extend U.S. bank supervision outside of our country. In
reality, it does no such thing. Rather, it is an effort to place
the U.S. operations of foreign banks in an appropriate context, using a systematic and consistent framework.
Consistent with this approach will be a series of
initiatives, including a new examination rating system for
U.S. branches and agencies of foreign banks, that several of
you may already have seen. Overall, the program focuses
more heavily than has been the case in the past on risk
management and internal control systems with respect to
both lending and capital market activities, similar to what
we’ve been doing increasingly in our examinations of U.S.
banking organizations.
In addition to providing U.S. bank supervisors
with a more logical approach to the supervision of foreign
bank activities, the new program should yield considerable
benefits to foreign banks. Most notably, foreign banks
should, over time, see a significant reduction in the burden
and duplication of supervisory efforts, as well as an
improvement in examination efficiency and focus.
Another positive development aimed at enhancing
the attractiveness of the United States to foreign banks is
the Federal Reserve’s program, initiated in March 1993, to
streamline the procedures foreign banks must follow when
making application to establish a presence in the United
States under the Foreign Bank Supervision Enhancement
I applaud the demise of the outmoded
restrictions on banks’ ability to do business
across state lines and believe it makes sense
for all banks and their customers.
Act of 1991. Under these procedures, the processing of
applications has been expedited and the burden on applicants reduced. Some of the key measures adopted, for
example, facilitate the process of checking on the backgrounds of shareholders and key personnel, conducting
concurrent reviews of applications by staff in Washington
and at the Reserve Banks, and jointly identifying deficiencies in the application and promptly communicating these
to the foreign bank. I’m well aware that there still is room
for further improvement in reducing bottlenecks that have
delayed applications. I can assure you that we are committed to continued progress and are working on achieving
further efficiencies in an area that has been difficult for all
of us.
Finally, I think it is worthwhile to note that the
banking climate in the United States has benefited greatly
from extensive communications between the supervisory
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
3
and legislative authorities. The Federal Reserve attaches
great importance to working closely with other bank
supervisors and legislators to craft policies and laws that we
believe will foster competition and increase flexibility in
the provision of financial services. At the same time, we are
intent on preserving our unyielding commitment to the
safety and soundness of the banking system. Continued
There can be no doubt that the better an
individual institution’s risk management
system is, the more efficiently it can deploy
its capital.
cooperation in pursuit of these common goals should help
ensure that the United States remains an attractive banking environment for foreign and domestic banks well into
the twenty-first century.
While there is much cause for satisfaction with
many of the measures already put in place, the future is not
without considerable challenge. One of the most important
challenges banks and supervisors face is to guard against a
significant weakening in credit standards. In the aftermath
of the 1990-91 stringency in credit, it was not surprising—and even desirable—to see some easing in credit
standards. Of late, however, it appears that increased competition among lenders for middle-market and large corporate business has produced a narrowing of margins and
additional relaxation in lending terms. Because experience
has shown that easing of standards can be and often is overdone, it is incumbent on lenders and supervisors to ensure
that future credit quality problems are avoided.
A second challenge banks and supervisors face is to
continue their efforts to encourage the development of
sound risk management practices in this period of rapid
financial innovation. There can be no doubt that the better
an individual institution’s risk management system is, the
more efficiently it can deploy its capital.
We at the Federal Reserve Bank of New York have
4
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
long encouraged innovation in financial instruments and
financial markets. Innovation increases competition,
improves market efficiency, and expands the variety of
products that can better serve customer needs. But with
innovation come increased responsibility and the need for
each financial institution, regardless of size, to engage in
prudent risk management practices to ensure that its activities remain consistent with its constantly evolving risk
profile.
Based on our experience, we believe that a successful risk management system should satisfy—at the least—
four basic principles:
• First, it should be subject to active oversight by
the board of directors and senior management of
the financial institution.
•
Second, it should embody well-conceived risk
identification measurement and reporting systems.
•
Third, it should include comprehensive internal
controls emphasizing the clear separation of
duties.
•
And, fourth, it should incorporate a well-defined
structure of limits on risk taking.
A review of some recent, well-publicized problem cases
clearly indicates that in each case there was a significant
failure in the design or implementation of one or more of
these basic principles.
I am pleased to note, however, that there seems to
be a consensus building in support of these basic principles
among a large group of internationally active banks, securities firms, end users, and their various supervisors. Last
year, both the Basle Committee on Banking Supervision
and the International Organization of Securities Commissions (IOSCO) issued papers addressing the need for sound
practices regarding the risk management of derivatives
activities. In March 1995, a private sector group representing the six largest securities firms in the United States
issued a paper indicating their voluntary adherence to similar practices. In addition, the Group of Thirty has put
forth two surveys and sets of recommendations on this
issue. And, from the supervisory side, examiner guidance
manuals on this subject have also been issued by the federal
banking regulators. But support for these principles, how-
ever gratifying, does not mean that our jobs are over. Innovation is an ongoing process and management procedures,
as well as supervisory practices, must continually adapt.
A third challenge for banks and supervisors has to
do with what I would call internal culture issues. These
issues involve the role of senior management and boards of
directors in the risk management process. Most of the wellpublicized problems of the recent past have also reflected
shortcomings in internal management processes.
Experience to date makes it all too clear that the
active involvement of a financial institution’s board of
directors and senior management is absolutely critical to
their ability to articulate and promote the requisite risk
management culture within their organizations. They
must be knowledgeable about the financial products their
institution is offering and the risks it is taking if they are
to give definition to the organization’s tolerance for risk
and provide leadership in its implementation.
Innovative financial instruments often are
extremely complex and can embody a variety of nontraditional risks. Therefore, no financial institution should be
engaging in activities its senior management does not adequately understand and its board of directors cannot oversee. This need for understanding the products and their risk
must extend to operating staff, auditors, and controllers.
Furthermore, senior management and boards of
directors must foster an environment of open communication at all levels of the organization. Such a dialogue is the
foundation of effective management supervision. A wellinformed management that encourages this communication will be in a better position to assess the contents of
daily internal monitoring reports and respond promptly
and appropriately to prevent a problem from emerging.
Honesty is another aspect of this internal culture.
The financial services business is traditionally one in which
integrity is essential. The most effective managers are
explicit about their commitment to fair business practice
and arm’s-length dealing in rules of conduct for employees,
and encourage the prompt communication of problems to
higher levels of management. This is more relevant today
than ever before. Competition is fierce. Markets can move
quickly; huge volumes can be traded in minutes, if not seconds, and end users have a wide choice of alternative institutions with which to do business. In this environment,
integrity is indispensable if institutions are to attract clients and retain their loyalty over the long run.
Finally, financial institutions must maintain open
lines of communication with their supervisors. Even in the
best-managed institutions, something can go awry. The
cumulative experience of the industry is that the sooner a
No financial institution should be engaging
in activities its senior management does not
adequately understand and its board of
directors cannot oversee.
problem is addressed, the better the chances of limiting its
financial and reputational impact. If a problem occurs, the
supervisors must be kept informed—not in order to micromanage the problem, but to be able to play a constructive
role in its resolution. The questions supervisors ask will
reflect their experience and their awareness of the potential
success or pitfalls of different strategies.
In sum, the environment for the banking industry
today is as vibrant as it has ever been. The range of opportunities for financial institutions to prosper and grow has
never been greater, as technology continues to shrink the
world, integrate markets, and open new avenues of potential profitability. In this environment, the real challenge
confronting both banks and their supervisors is to balance
the risks with the rewards. To do so requires commitment
and vigilance on all our parts—supervisors and supervised—to an ongoing process of dialogue, accountability,
and cooperation.
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
5
Correlation Products and
Risk Management Issues
James M. Mahoney
The views expressed in this article are those of the authors and do not necessarily reflect the position of the Federal
Reserve Bank of New York or the Federal Reserve System.
The Federal Reserve Bank of New York provides no warranty, express or implied, as to the accuracy, timeliness, completeness, merchantability, or fitness for any particular purpose of any information contained in documents produced
and provided by the Federal Reserve Bank of New York in any form or manner whatsoever.
T
he large, highly publicized losses incurred by
some financial institutions in recent years have
caused the press and financial regulators to
examine the practice of risk management
more closely. In particular, institutional losses have raised
concerns about the accuracy of the techniques used to
assess the risk of an institution’s portfolio. While largely
effective when applied to traditional financial portfolios,
these techniques are not always successful in capturing the
complex configurations of risk inherent in today’s highly
customized derivative products. This article examines correlation products, one such class of derivative instruments,
which are challenging the traditional measures of price risk.
“Price risk” is defined as the risk that the value of
an institution’s entire portfolio will change as a result of
shifts in market conditions. Market conditions comprise
risk factors (also referred to as “state variables”) such as foreign exchange rates, equity prices, interest rates, and commodity prices. In traditional products, or “plain vanilla”
instruments, price risk is separable. In other words, the sen-
sitivity of the traditional portfolio’s value to one risk factor
is independent of the level of another risk factor. The price
risk of these portfolios can be estimated by measuring their
sensitivity to individual risk factors and aggregating these
sensitivities to arrive at an overall risk profile.
In correlation products, however, price risk is nonseparable—that is, a change in one risk factor will affect the
price sensitivity of another risk factor. Thus, the pricing,
hedging, and risk management of these instruments
depend on the correlations between the various risk factors
rather than on the analysis and aggregation of the individual variables. Because traditional risk management tools do
not account for the interdependency of the risk factors, traditional measures of overall price risk may be inaccurate for
portfolios that contain correlation products.
This article defines correlation products and
explores the problems they raise for risk management systems in financial institutions. It explains the difficulties of
analyzing nonseparable risk in one type of correlation product, the differential (diff) swap, and describes the much
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
7
simpler measurement of separable risks in a standard constant maturity Treasury swap. The article concludes with
some general ways nonseparable risk can be managed.
DEFINING CORRELATION PRODUCTS
Financial instruments can be characterized by the legally
binding cash flows that they generate. A correlation product is defined by two characteristics of its cash flow. First,
the cash flow must be a function of at least two risk factors.
Second, at least two of these risk factors must be combined
in a non-additive way.1 The following expressions compare
the cash flows of instruments with separable risks to those
with nonseparable risks:
(1)
separable risk: CF ( x 1, x 2 ) = CF ( x 1 ) + CF ( x 2 )
nonseparable risk: CF ( x 1, x 2 ) = CF ( x 1 ) × CF ( x 2 ) ,
where CF(.) represents the cash flow generated by a security as a function of risk factors x1 and x2. The risk factors
in the separable risk expression are broken into two separate terms that are summed, while the risk factors in the
nonseparable risk expression form a single product and
cannot be so separated.2
Common forms of correlation products include
diff swaps and quanto swaps.3 (Several other types of correlation products are highlighted in Appendix I.) Both swaps
In correlation products, . . . price risk is
nonseparable—that is, a change in one risk
factor will affect the price sensitivity of
another risk factor.
are examples of cross-currency basis trades—that is, trades
whose cash flows depend on the difference between the levels of two risk factors. In a diff swap, the risk factors are a
floating domestic interest rate and a floating foreign interest rate, but unlike standard cross-currency trades, both
payments are made in a single currency. Both payments are
8
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
also based on the same fixed notional principal value with a
set maturity and are made according to the term of the
interest rate indexes. For instance, if six-month LIBOR is
used for the interest rate index, cash flows would be
exchanged every six months. Unlike some standard crosscurrency swaps, diff swaps do not require principal payments at the origination and termination of the swap,
because all cash flows are denominated in a single currency.
The structure of a quanto equity swap is similar to
that of a diff swap. The foreign floating interest rate payment, however, is replaced with a payment based on a foreign equity index return such as the Nikkei index.
In both diff swaps and quanto swaps, the dealer
commits to paying a floating foreign rate on a fixed U.S.
dollar notional principal amount rather than on a fixed
amount in the foreign currency. This commitment exposes
the dealer on the foreign leg of the correlation product to a
variable notional principal amount that changes whenever
the exchange rate or the foreign index fluctuates.
THE DEMAND FOR CORRELATION
PRODUCTS
The market for correlation products is small compared
with the plain vanilla market, estimated to have notional
values of trillions of U.S. dollars (Remolona 1992-93).
Nevertheless, the market for correlation products represents a growing portion of the overall market for securities
that trade over the counter rather than on organized
exchanges. End-user demand appears to be the driving
force behind this growth. Why are end users drawn to correlation products? To be sure, some investors are in the
market purely as speculators. End users and dealers alike,
however, cite several nonspeculative reasons for their interest in correlation products.
First, end-user demand for correlation products
can stem from the same type of economic analysis that
drives other investment decisions. For example, a U.S. dollar investor who believes that a foreign equity market is
undervalued because of some underlying weakness in the
country’s economy may be reluctant to face the foreign
exchange exposure involved in buying the foreign equities
directly. In this case, a quanto swap—in which the end
user pays U.S. dollar LIBOR in U.S. dollars and receives
the foreign index return in U.S. dollars—would allow the
investor to express confidence in foreign equities at the
same time that it protects him or her from unfavorable
changes in foreign exchange rates.
Second, investors may desire to gain the benefits of
international equity or bond diversification without being
Because traditional risk management tools do
not account for the interdependency of the risk
factors, traditional measures of overall price risk
may be inaccurate for portfolios that contain
correlation products.
subject to the foreign exchange exposure that would occur
if the domestic currency appreciates against the currencies
whose assets are being held. This currency risk may be
unacceptable if the investor faces large future liabilities in
the domestic currency (such as retirement expenses). Of
course, the investor would have to weigh the potential benefits of diversification against the costs of these swaps.
Third, some individuals and institutions use
derivative securities to circumvent (sometimes selfimposed) restrictions on holdings. For instance, the investment committee of a pension fund or insurance company
may require all investments to be denominated in the
domestic currency. While this rule would prohibit direct
foreign capital market holdings, the managers of these
investments could gain exposure to foreign debt or equity
markets through correlation products such as diff swaps or
quanto swaps.
Fourth, an end user may negotiate a correlation
product with a dealer rather than dynamically create a similar exposure because dealers have a competitive advantage
over end users in managing the complex exposures of correlation products. Dealers’ advantages include their ability to
trade at smaller bid-ask spreads in the cash market and
their greater experience in negotiating within the legal
environments of foreign economies, particularly in the
emerging debt and equity markets. In addition, dealers’
risk management systems tend to be more advanced than
most end users’ systems.
One use for which correlation products are generally not appropriate is the hedging of risks arising from traditional products. Most hedgers have little interest in
correlation products because the type of exposure found in
them is not available in existing cash or derivative securities. Asset managers are more likely to use these products
in an effort to outperform an index or other return measure.
AN EXAMPLE OF A CORRELATION
PRODUCT: THE DIFF SWAP
THE MARKET FOR DIFF SWAPS
One of the first reported diff swaps was negotiated in early
1991 between Credit Suisse First Boston and a Japanese
insurance company. Since that time, diff swaps have grown
rapidly in popularity, reportedly because of the large differential in short-term interest rates across major currencies.
Today, diff swaps make up a significant portion of the
exotic instruments market. A recent estimate places the
The market for correlation products represents
a growing portion of the overall market for
securities that trade over the counter.
notional principal amount of diff swaps outstanding at
$40 billion to $50 billion.4
Through the use of diff swaps, investors in currencies with low yields attempt to enhance their returns by
swapping into currencies with higher yields. Diff swaps
have been transacted in a wide range of currency pairs,
including U.S. dollar LIBOR against LIBOR rates of the
deutsche mark, British pound, Swiss franc, and Australian
dollar, and LIBOR rates of the deutsche mark and Swiss
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
9
franc against LIBOR rates of the Italian lira, Spanish
peseta, and other high-yielding currencies of the European
Exchange Rate Mechanism.
Despite the rapid growth of the diff swap market,
it is still controlled by only a handful of dealers. The main
barrier to entry for other derivatives dealers is the expertise
needed to price, hedge, and manage the nonseparable risks
present in these instruments. Unlike traditional instru-
Through the use of diff swaps, investors in
currencies with low yields attempt to enhance
their returns by swapping into currencies with
higher yields. Diff swaps have been transacted
in a wide range of currency pairs.
ments, correlation products require risk managers to
account for nonseparable risks by making assumptions
about the future correlations between risk factors.
of hedging the exposure is less than the price that the
dealer offers to the counterparty, the counterparty will take
the business to a dealer with more competitive prices.
Therefore, the price must be equal to the cost of hedging.
Although this approach does not consider market realities
such as transaction costs, liquidity considerations, and
model risk, it yields a reasonable approximation to the
value of a security.
HEDGING AND PRICING A DIFF SWAP
Suppose a dealer has entered into a diff swap in which for a
period of one year it receives six-month U.S. dollar LIBOR
in U.S. dollars while it pays six-month deutsche mark
LIBOR in U.S. dollars to the end user. The semiannual
interest payments are based on a $100 million notional
principal amount and are settled in arrears (Exhibit 1).5
To value the cash flows of the diff swap, the dealer
must determine the level of the cash flows that will take
place in the future (in this case, in six months’ and in one
year’s time) and discount these flows to the present.6
Therefore, the present value of the diff swap can be written
as
(2)
PV of the diff swap =
PV6 mo ($100m × (r
@ t = today
US $– L I B O R
– r
@ t = today
DM – L I B O R
))
ANALYZING THE PRICE RISK OF A DIFF SWAP
The complex procedures for analyzing the price risk of diff
swaps are explained below. Readers may wish to compare
these procedures with the relatively simple process of analyzing the price risk of a standard derivative instrument,
the constant maturity Treasury (CMT) swap, outlined in
Appendix II.
Both the diff swap and CMT swap examples rely
on the assumption that markets are competitive. Thus, we
determine the price of the instrument by estimating the
cost to the dealer of hedging the resulting risk exposures.
This does not mean that the dealer will (or should) hedge
the resulting exposure. Rather, we determine the price of
an instrument by ruling out the only other alternatives. If
the cost of replicating the exposures is greater than the
price that the counterparty offers to the dealer, the dealer
will not enter into the trade. At the same time, if the cost
10
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
+ PV12mo ($100m × ( r̃
@t =
6mo
US $– LI B O R
– r̃
@t =
6mo
))
DM – L I B O R
where PVt(CF) indicates the present value of a cash flow,
CF, occurring at time t, and r xt represents the prevailing
interest rate in market x at time t.
The value of the cash flow that will occur in six
months’ time (the first term in equation 2) is easy to calculate. The parties swap the difference between the current value of U.S. dollar LIBOR and deutsche mark
LIBOR paid in U.S. dollars on a notional principal
amount of $100 million. The cash flow will not change
when interest rates or exchange rates fluctuate, and the
cash flows can be discounted at the risk-free U.S. dollar
six-month interest rate.7
However, the value of the cash flow that will occur
in twelve months’ time (the second term in equation 2) is
more difficult to calculate. The dealer cannot convert the
deutsche mark liability embedded in the swap into a U.S.
dollar liability, because the level of deutsche mark exposure
faced at the swap initiation will be determined by the level
of deutsche mark LIBOR and the deutsche mark/U.S. dollar exchange rate in six months’ time. Thus, while typical
hedging instruments protect against exposure by converting a fixed principal amount from one currency to another,8
the exposure faced by the dealer in a diff swap involves a
floating deutsche mark principal. Ultimately, the lack of a
static hedge forces the dealer to make assumptions concerning the future correlation between the deutsche mark/
U.S. dollar exchange rate and deutsche mark LIBOR and to
update the hedging position dynamically.
ESTIMATING THE COST OF HEDGING THE EXPOSURES
Once the cash flows of the diff swap are determined, the
dealer estimates the cost of hedging the floating interest
rate exposures by observing the costs of entering into two
plain vanilla interest rate swaps—one in U.S. dollars and
one in deutsche marks. These interest rate swaps, which are
based on the notional principal amount of the diff swap in
U.S. dollars or its dollar equivalent in deutsche marks, will
have the same maturity and payment dates as the diff swap
(Exhibit 2). Because the market for interest rate swaps is
highly competitive, we can assume that these two hedging
swaps will be entered into at a net present value of zero. As
a result, the overall value of the diff swap will be the same
before and after hedging. However, the combination of the
diff swap and the two hedging swaps does not eliminate all
price risk. The presence of residual risk suggests that the
market prices of existing securities alone are not enough to
determine the value of the diff swap.
ACCOUNTING FOR RESIDUAL EXPOSURES
To account for residual risk, the dealer must assess the
joint probability distribution of the deutsche mark/U.S.
dollar exchange rate and the deutsche mark LIBOR rate.
For the purposes of this example, assume that the U.S.
dollar term structure is flat at 6 percent, the deutsche
mark term structure is flat at 8 percent, and the current
deutsche mark/U.S. dollar exchange rate is 1.6. Exhibit 3
shows the gross cash flows and the net cash flows to and
from the dealer.
To determine the value of the residual exposure
that occurs in one year, the dealer converts the net cash
flows into U.S. dollars at the exchange rate prevailing at
Exhibit 2
DIFFERENTIAL SWAP: AFTER
INTEREST RATE SWAPS
Exhibit 1
DIFFERENTIAL SWAP:
GENERIC CASH FLOWS
Diff Swap
Counterparty
Six-month US$ LIBOR
x $100 million
(in US$)
Diff Swap
Counterparty
Six-month US$ LIBOR
x $100 million
(in US$)
Six-month DM LIBOR
x $100 million
(in US$)
Hedge
Counterparty
#1
Swap Dealer
DEALER HEDGES WITH
Six-month
US$ LIBOR
(in US$)
Six-month DM LIBOR
x $100 million
(in US$)
Swap
Dealer
Fixed DM
(in DM)
Fixed US$
(in US$)
Six-month
DM LIBOR
(in DM)
Hedge Swap #1
Hedge Swap #2
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
Hedge
Counterparty
#2
11
t=6 months, q̃ DM/$:
(3)
$100m × (6% - r̃ DM LIBOR)
+ DM160m × ( r̃ DM LIBOR/ q̃ DM/$ - 8%/ q̃ DM/$),
which can be simplified to:
(4)
($100m - DM160m / q̃ DM/$) × (8% - r̃ DM LIBOR)
- $100m × 2%.
As shown in expression 4, the residual cash flow
contains a risky component (first term) and a fixed component (second term).9 The cash flow represented by the second term is easy to value: it represents a fixed cash flow on
a fixed date in a single currency and therefore can be discounted at the one-year spot rate at time zero. However,
the cash flow represented by the first term is difficult to
value because two sources of risk are being combined in a
single term. This first term fits the definition of nonseparable risk: the two random variables, q̃ $/DM and r̃ DM LIBOR,
are multiplied rather than summed or differenced and
therefore cannot be separated into different terms.
Traditional risk management tools properly measure the risk of correlation products only if risk factors do
not fluctuate simultaneously. For example, if the exchange
rate remains at 1.6 deutsche marks per U.S. dollar, then the
first term of expression 4 will equal zero and the resulting
cash flow will be zero, regardless of the level of deutsche
Exhibit 3
CASH FLOWS OF A DIFF SWAP TO AND FROM DEALER
All cash flows take place at t=12 months based on rates at t=6 months
Diff swap:
Inflow:
Outflow:
Hedge swap #1:
Inflow:
Outflow:
Hedge swap #2:
Inflow:
Outflow:
Net cash flows:
Inflow:
Outflow:
12
$100 million x r̃ US$ LIBOR
$100 million x r̃ DM LIBOR
$100 million x 6%
$100 million x r̃ US$ LIBOR
DM 160 million x r̃ DM LIBOR
DM 160 million x 8%
$100 million x 6% + DM 160 million x r̃ DM LIBOR
$100 million x r̃ DM LIBOR + DM 160 million x 8%
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
mark LIBOR. At the same time, if deutsche mark LIBOR
remains at the fixed interest rate of 8 percent, the cash flow
will be zero regardless of the level of the deutsche mark/
U.S. dollar exchange rate (Exhibit 4). These zero cash flows
show that the dealer’s position is hedged for movements in
either deutsche mark LIBOR or deutsche mark/U.S. dollar
exchange rates. However, the dealer is not hedged against
simultaneous movements.10
Simultaneous movements in the foreign index and
the exchange rate will determine the sign—positive or
negative—of the cash flow. For example, let us assume that
the deutsche mark LIBOR decreases and the deutsche
mark/U.S. dollar exchange rate increases (the deutsche
mark depreciates relative to the U.S. dollar). Because the
movements in the deutsche mark LIBOR and the deutsche
mark/U.S. dollar exchange rate are negatively correlated,
both terms in expression 4 will be positive, and the dealer
will receive a positive cash flow. Conversely, if the deutsche
mark LIBOR decreases and the deutsche mark/U.S. dollar
exchange rate decreases (the deutsche mark appreciates relative to the U.S. dollar), then the cash flow to the dealer
will be negative. Therefore, the correlation between the risk
factors determines whether the cash flow of the diff swap
will be positive or negative. Using the data in Exhibit 4,
the chart on page 13 offers a graphic representation of the
concept of nonseparability.
In summary, while part of the exposure of the diff
swap can be hedged with existing securities, residual risk
must be evaluated in order to determine the value of the
diff swap. An important, and complex, component of the
residual risk is the correlation between the risk factors.
IMPLICATIONS FOR RISK MANAGEMENT
AND SUPERVISORY PRACTICES
The most fundamental problem in estimating the price
risk of correlation products occurs at the operational level.
The feature of nonseparability means that a dealer cannot
break up the price sensitivity of diff swaps or other correlation products into component risks and then assign each
risk to its corresponding business function. Instead, an
institution’s trading desks need to coordinate their activities by establishing formal systems of communication
among trading units and between trading units and global
risk managers. This level of coordination has not been
required in managing traditional instruments, and it may
entail substantial changes in an institution’s management
approach and structure.
Of course, the potential for problems at the operational level does not stop there. The portfolios of large
institutions can comprise thousands of individual trading
positions across multiple trading desks in several geographic locations. To arrive at a comprehensive estimate of
risk, most of these institutions rely on summary statistics
of each trading position. They then aggregate these summary statistics to arrive at the risk of the entire firm.11
Because traditional measures of risk do not accurately
reflect the risk of a portfolio that contains correlation products, these summary statistics can misguide corporate decisions. For example, an underestimation of price risk, if
large enough, could lead a financial institution to hold less
than the optimal amount of capital against potential losses.
Inaccurate estimates can also influence the financial decisions of market participants. Transparency of risks
and exposures is an important feature of an institution’s
financial statements (Bank for International Settlements
Exhibit 4
CASH FLOW
1994). If the portfolio of an institution contains significant
levels of “hidden” correlation risk, then investors may not
efficiently allocate capital to that institution. For instance,
a lack of transparency of risk can inhibit the flow of capital
to a healthy financial institution that is experiencing a
temporary liquidity crisis.
PROFILE FOR DIFF SWAP DEALER
Cash flow occurring in year one for diff swap on $100 million notional principal based on DM LIBOR and DM/U.S. dollar exchange rate in six months:
($100m - DM 160m / q̃ DM/$) x (8% - r̃ DM LIBOR ).
The value of the expression is halved when calculating the cash flows because the diff swap is assumed to have semiannual payments. The following matrix shows the level
of cash flow (in millions of dollars) for various possible realizations of the exchange rate and the DM LIBOR rate.
DM LIBOR
(Percent)
12
11
10
9
8
7
6
5
4
1
1,200,000
900,000
600,000
300,000
0
(300,000)
(600,000)
(900,000)
(1,200,000)
Cash Flow (Millions of Dollars)
Exchange Rate (DM/U.S. dollar)
1.2
1.4
1.6
1.8
666,667
285,714
0
(222,222)
500,000
214,286
0
(166,667)
333,333
142,857
0
(111,111)
166,667
71,429
0
(55,556)
0
0
0
0
(166,667)
(71,429)
0
55,556
(333,333)
(142,857)
0
111,111
(500,000)
(214,286)
0
166,667
(666,667)
(285,714)
0
222,222
2
(400,000)
(300,000)
(200,000)
(100,000)
0
100,000
200,000
300,000
400,000
2.2
(545,455)
(409,091)
(272,727)
(136,364)
0
136,364
272,727
409,091
545,455
Note: The unshaded regions represent the cash flows of a diff swap resulting from changes in individual risk factors.
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
13
From a supervisory perspective, the market for
correlation products raises several concerns. First, because
the development and execution of correlation products are
highly concentrated within the banking community, a
shift in market conditions could have potentially adverse
consequences for a small number of large institutions.
Moreover, some correlation products are structured in the
risky, illiquid currencies of emerging markets, where large
changes in interest rates and exchange rates can occur overnight or, significantly for correlation products, simultaneously. For example, in 1994, the Mexican peso/U.S.
dollar exchange rate, the Mexican equity markets, and
Mexican interest rates changed dramatically and concurrently over a short period of time. Although nonseparable
tional instruments, the end user of a correlation product
must find a counterparty who is willing to take on the
exact exposure of the original contract in order to counteract the existing contract; otherwise, he or she may be compelled to hedge the exposure dynamically. Therefore, if
liquidity for correlation products dries up, end users may
be forced into dynamically hedging exposures that they
would like to eliminate but cannot because of a lack of
counterparty interest. The fact that the market for correlation products is predominantly demand-driven adds to
future liquidity concerns. If demand diminishes, financial
institutions will have little incentive to maintain an active
secondary market.
MANAGING NONSEPARABLE RISK
The feature of nonseparability means that a
dealer cannot break up the price sensitivity of
diff swaps or other correlation products into
component risks and then assign each risk to its
corresponding business function.
structures can provide valuable liquidity to otherwise inaccessible markets, risks may be greatly underestimated in
these more volatile environments.
Second, nonseparable risk is one of many factors
that may affect the implementation of regulatory capital
requirements. The Bank for International Settlements
(1995) has recently put forth a proposal that would allow
individual financial institutions to use their own internal
models to assess risk and to assign regulatory capital
requirements. Internal models, if properly constructed,
should be able to accurately reflect the effects of nonseparable risks on the institution’s portfolio.
Finally, liquidity of the market may be at risk
because the exposures of a correlation product may be difficult to reverse if the counterparty is not willing to cancel
the contract at a fair value. Unlike the investor in tradi-
14
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
As shown by the price risk analysis of a diff swap, traditional risk measures can understate the amount of risk
present in correlation products. How can institutions
enhance risk management tools to address this potential
problem? The first step is to identify the presence of nonseparable risks in a portfolio. Two approaches might be
taken:
• Each variable to which a portfolio is exposed may be
shocked individually and the sum of these changes in
market value compared with the changes brought
about when the variables are shocked simultaneously.
If the change in value stemming from the simultaneous shock differs from the sum of the effects of the
individual shocks, then the portfolio contains nonseparable risks.
• Ex post profits and losses and model-predicted profits
and losses may be reconciled, taking into account the
realized level of the risk factors. A risk manager could
investigate the cause of profits or losses in excess of
predictions by analyzing discrepancies between model
prices and market prices. Such excess returns could
arise if nonseparable risk is being measured by traditional risk management tools.
Once nonseparable risks are identified, the risk
manager could then use a simulations-based approach to
measure price risk. This type of approach requires a number of time-consuming, expensive steps, as outlined below.
A risk manager first identifies the risk factors to
which a portfolio is exposed, collects historical data on
these factors, and analyzes and models the volatility of the
factors and their relationships to each other. Unfortunately,
historical data series do not always exist, particularly for
newly developed markets or economies. Alternatively, a
risk manager may use current market prices (such as
options prices), if available, to derive market-implied estimates of future volatilities. A third option is to rely on the
data set for a risk factor similar to that under investigation.
For example, a risk manager may estimate a current exposure to an emerging economy by using data from a country
whose economy has undergone a similar transformation.
Next, the risk manager generates a large number
of future paths for the risk factors through one, or a combi-
Once nonseparable risks are identified, the risk
manager could then use a simulations-based
approach to measure price risk.
nation, of two methods—a model-based approach or an
empirical-based approach. The former assumes a structure
for the data, for example, a multivariate normal distribution or generation by a time-varying volatility process such
as an ARCH-type process. The latter uses historical data to
create a frequency distribution, or histogram, with which
the future distribution of the risk factors is assumed to
coincide. The model-based approach has the advantage of
simulating an unlimited number of future paths, but the
model may be misspecified or incorrect (introducing model
risk). The empirical-based approach frees the researcher from
a potentially incorrect model, but its use is often limited by
the lack of reliable historical data on many risk factors.
After generating future paths for the risk factors,
the manager computes the future value of each security
under the various scenarios and estimates the present value
of the security as the average discounted value of the simulated future values. This averaging procedure assumes that
each of the simulated scenarios is equally likely. Finally, the
manager calculates estimates of price sensitivities by “perturbing” each path taken by the risk factors and recalculating the value of the portfolio. The change in the value of
the portfolio divided by the perturbation is a measure of
the delta (the rate of change of the portfolio to a risk factor). Pair-wise perturbations and revaluations yield estimates of price sensitivities to changes in pairs of risk
factors.
Because the process is so involved, a simulationsbased approach seems appropriate only for firms that place
great emphasis on nonseparable products. Such firms will
probably find it useful to develop multiple simulation
methodologies (using variations of both the empiricalbased and model-based approaches) to ensure that their
risk estimates are robust to alternative specifications.
CONCLUSIONS
Correlation products, a new class of derivatives instruments, are challenging the effectiveness of existing techniques for measuring price risk. For traditional portfolios,
financial institutions evaluate individual risk factors at the
trading-unit level and subsequently aggregate the units’
estimates to arrive at an accurate risk profile. For correlation products, however, this technique is not effective
because the sensitivity of one risk factor is always a function of the level of another risk factor. Thus, because many
institutions continue to rely solely on traditional risk management tools, nonseparable risks may go unmeasured.
The potential for understated risk raises several
concerns regarding financial institutions’ regulatory capital
requirements, financial disclosure practices, and supervisory activities. Techniques to capture nonseparable risks—
such as the simulations-based approach outlined in the
article—can help address these concerns by augmenting
traditional risk measures. Given the tremendous rise in
financial innovation, new types of risk are likely to prompt
an increasing number of financial institutions to reexamine
and enhance risk management practices.
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
15
APPENDIX I: COMMON FORMS OF CORRELATION PRODUCTS
In addition to diff swaps and quanto swaps, several new
types of correlation products have been developed in
recent years.
Correlation products include any contract that
pays off as a function of the minimum or maximum of two
random processes. Specific contracts include the option to
trade one asset for another and the outperformance option,
which pays some function of the maximum of two indexes,
such as stock market indexes. In addition, relative value
derivatives, which pay off as a function of the ratio of two
variables, appear to be gaining popularity (see, for example, Locke 1995 and Elms 1995).
Correlation effects may also be embedded in more
exotic structures. Quanto options—that is, options on a
foreign index with the spot and strike prices denominated
in a foreign currency but cash flows taking place at a fixed
exchange rate in the domestic currency—have become
increasingly popular.12 Also gaining ground are correlation products in the form of a binary option,13 where the
payoff of the option depends on two underlying variables.
A hypothetical correlation binary call option would pay a
predetermined constant amount, X, if the (constant maturity) three-month U.S. dollar interest rate, r, is above r* and
a foreign/U.S. dollar exchange rate, q, is above q* (that is,
the payoff is {X if r > r* and q > q*; 0 otherwise}). This
16
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
exotic binary option is simultaneously bullish on the U.S.
dollar relative to the foreign currency and bearish on U.S.
dollar interest rates. Its value will depend on the anticipated
correlation between the three-month U.S. dollar interest
rate and the foreign currency/U.S. dollar exchange rate.
Certain yield curve trades also involve nonseparable risk. A call option on a short-term interest rate, with
the strike determined by a long-term interest rate, is an
example of a nonseparable yield curve trade.
In addition, Asian options with geometric means
for the spot price or the strike price fit the definition of correlation products. An example is an option on a stock index
for the time period [0, T] with strike price K and a payoff
that is a function of the geometric mean of the index level
taken at T+1 discrete dates:
CFT = max [0, (S0 × S1... × ST)1/(T+1)-K].
The cross partial of the value of this option, ∂2V/∂Ss∂St, is
not zero for s≠t; therefore the value of this option will be a
function of the correlation matrix of S, which is effectively
the autocorrelation structure of the process for S. If the
option payoff were a geometric average across securities
instead of across time, the option on the index would be a
function of the entire covariance matrix of stock prices.14
APPENDIX II: ANALYZING THE PRICE RISK OF A STANDARD INSTRUMENT:
THE CONSTANT MATURITY TREASURY SWAP
Suppose a securities dealer has entered into a constant
maturity Treasury (CMT) swap with a notional value of
$100 million. For a term of one year, the dealer pays the
current five-year U.S. Treasury rate on a notional value of
$100 million and receives the current ten-year Treasury
rate on a notional value of $100 million. The dealer obviously benefits if the yield curve steepens (Exhibit A1).
DETERMINING AND VALUING THE CASH FLOWS
Exhibit A2 illustrates the cash flows of this simple portfolio as a function of the five-year Treasury rate and the tenyear Treasury rate. This “five-by-ten CMT swap” shows
separable risk in the two risk factors: the sensitivity of the
flows to changes in the five-year Treasury rate is independent of the level of the ten-year Treasury rate; the sensitivity of the cash flows to changes in the ten-year Treasury
rate is independent of the level of the five-year Treasury
rate. To value the CMT swap, the dealer breaks the resulting risks into the five-year and ten-year components, then
values these components separately and aggregates them.
Because the risks of the CMT swap are separable,
Exhibit A1
INTEREST
the dealer can break up the risks and assign them to two
different trading units—for example, the unit responsible
for trading in the five-year Treasury sector and the unit
responsible for trading in the ten-year Treasury sector.
These two trading units would not need to coordinate their
efforts.
ESTIMATING THE COST OF HEDGING THE
EXPOSURES
Exhibit A3 shows how the dealer may attempt to hedge
(and therefore assign a price to) the exposures of the
resulting trade. For the five-year Treasury exposure, the
trader uses interest rate forward contracts, which require
him or her to pay a fixed rate in exchange for the CMT
five-year Treasury rate. For the ten-year Treasury exposure, the trader uses an interest rate swap based on the
ten-year Treasury rate, which requires him or her to pay
the ten-year CMT rate in exchange for a fixed rate. As a
result, exposures to the five-year and ten-year Treasuries
are eliminated, and the pricing of the CMT swap amounts
to the pricing of two riskless fixed flows in the future
(Exhibit A4). We can conclude that the price sensitivity
of the CMT swap is similar to the price sensitivities of
RATE SWAP: GENERIC CASH FLOWS
Exhibit A2
CASH FLOWS OF A CMT SWAP TO AND FROM DEALER
All cash flows take place at t=12 months based on rates at t=6 months
Interest Rate Swap
Counterparty
Ten-year
U.S. Treasury rate
x $100 million
Five-year
U.S. Treasury rate
x $100 million
Swap Dealer
APPENDIX
Five-by-ten CMT swap:
Inflow:
Outflow:
Hedge swap:
Inflow:
Outflow:
Hedge forwards:
Inflow:
Outflow:
Net cash flows:
Inflow:
Outflow:
$100 million x r̃10
$100 million x r̃5
$100 million x rFIXED1
$100 million x r̃10
$100 million x
$100 million x
r̃5
rFIXED2
$100 million x
$100 million x
rFIXED1
rFIXED2
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
17
APPENDIX II: ANALYZING THE PRICE RISK OF A STANDARD INSTRUMENT:
THE CONSTANT MATURITY TREASURY SWAP (Continued)
fixed-for-floating swaps on a five-year Treasury rate and a
ten-year Treasury rate.15 Using the data in Exhibit A4, the
chart on this page offers a graphic representation of the
concept of separability.
Exhibit A4
CASH FLOW
PROFILE OF A CMT SWAP
The cash flow of a five-by-ten CMT swap is the notional value of the swap times
the difference between the most recently issued ten-year Treasury and the most
recently issued five-year Treasury:
CF = $100m x (r10 - r5),
REVIEWING THE LACK OF RESIDUAL EXPOSURES
A lack of residual exposures once the two hedging strategies are implemented indicates that two other instruments—interest rate swaps and interest rate forwards—
serve the same economic function as a CMT swap. These
instruments can be used as alternate hedging vehicles if the
market for CMT swaps becomes illiquid. Lack of residual
exposure also indicates that the pricing and hedging of a
five-by-ten CMT swap is fully determined by markets for
the individual five-year and ten-year risks. In summary,
because risk is separable, the pricing and hedging of the
CMT swap does not require the dealer to estimate the correlation coefficient between the two risk factors.
where the notional principal is assumed to be $100 million, and r5 and r10 represent the five-year and ten-year Treasury rates, respectively. The following
matrix shows the level of cash flow (in millions of dollars) for various possible
realizations of the five-year and ten-year Treasury rates at the next payment date.
Cash Flow (Millions of Dollars)
Five-Year Treasury Rate
Percent
9
8
Ten-Year
7
Treasury
6
Rate
5
4
3
3
6.0
5.0
4.0
3.0
2.0
1.0
0.0
4
5.0
4.0
3.0
2.0
1.0
0.0
-1.0
5
4.0
3.0
2.0
1.0
0.0
-1.0
-2.0
6
3.0
2.0
1.0
0.0
-1.0
-2.0
-3.0
7
2.0
1.0
0.0
-1.0
-2.0
-3.0
-4.0
8
1.0
0.0
-1.0
-2.0
-3.0
-4.0
-5.0
9
0.0
-1.0
-2.0
-3.0
-4.0
-5.0
-6.0
Note: The unshaded regions represent the cash flows of a CMT swap resulting
from changes in individual risk factors.
Exhibit A3
INTEREST RATE SWAP: AFTER DEALER HEDGES
WITH INTEREST RATE SWAP AND FORWARDS
Interest Rate Swap
Counterparty
Ten-year
U.S. Treasury rate
x $100 million
Hedge
Counterparty
#1
18
Ten-year
Treasury rate
x $100 million
Five-year
U.S. Treasury rate
x $100 million
Swap
Dealer
Fixed rate
x $100 million
Hedge
Counterparty
#2
Fixed rate
x $100 million
Five-year
Treasury rate
x $100 million
Hedge with Swap
Hedge with Forwards
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
APPENDIX
ENDNOTES
1. The term “correlation product” can be misleading because it refers to
the structure of the instrument, not to the correlations between the risk
factors. If the cash flows of a product cannot be separated into different
terms, the instrument is nonseparable and therefore a correlation
product.
2. It is not the estimation of the correlations between market risk factors
that confounds traditional risk management systems. Indeed, most risk
management tools require correlation estimates. Rather, the assumption
of separability inherent in most traditional risk management tools leads
to the underestimation of risk in correlation products. The nonseparable
expression cited in the text shows that the correlation between the risk
factors x1 and x2, usually denoted ρx x , does not enter into the definition
1 2
of a correlation product.
3. Diff swaps are also referred to in the trade press as quantity-adjusted
swaps (quants), guaranteed exchange rate swaps, LIBOR differential
swaps, cross index basis (CRIB) swaps, and switch-LIBOR swaps.
4. For a description of the early development of the diff swap market, see
Shirreff (1992), Cookson (1992), and Das (1992a, 1992b).
5. Settlement in arrears for a one-year swap with semiannual payments
means that the first payment, made in six months’ time, is based on the
current values of LIBOR, and the second (and last) payment, made in one
year’s time, is based on the values of LIBOR realized in six months’ time.
6. Several authors, including Jamshidian (1993) and Wei (1994), have
derived formulas for the present value of a diff swap. These formulas are
contingent on the assumed process of the term structure, a complex
subject that is not treated in this article.
7. The flows are considered riskless because throughout this paper we
assume that there is no counterparty credit risk.
10. When separable risks are present, a dealer hedged against movements
in individual risk factors would necessarily be hedged against
simultaneous movements in risk factors.
11. Standard summary statistics include the positions’ current market
values, deltas (market value sensitivities to underlying risk factors),
gammas (sensitivities of the deltas to underlying risk factors), vegas
(market value sensitivities to volatility changes), and thetas (market
value sensitivities to the passage of time).
12. Quanto Nikkei put warrants, the focus of a study by Dravid,
Richardson, and Sun (1993), began trading on the American Stock
Exchange in 1992.
13. A plain vanilla binary call option is a derivative security that pays
nothing if the underlying asset price or rate, S, finishes at or below the
strike price of the option, K, and pays off a predetermined, constant
amount, X, if the asset finishes above the strike price (that is, the payoff
is {X if S > K; 0 if S ≤ K}). Binary options are also called all-or-nothing
options, bet options, and lottery options.
14. An interesting example of such a contract is the now-defunct Value
Line Index Futures contract at the Kansas City Board of Trade (KCBOT)
(Thomas 1994). The Value Line Index that was used to determine the
delivery price of the contract was a geometric average index, which meant
that the appropriate arbitrage model was not the standard cost-of-carry
model but rather a dynamic strategy depending on the entire covariance
matrix of the stocks in the index. The KCBOT contract failed after other
exchanges introduced newer futures contracts based on the arithmetic
mean of the components (such as the Standard & Poor’s contracts). The
newer futures contracts are much more easily replicated in the cash
market because the covariance matrix of their components does not need
to be estimated.
8. For instance, currency futures and forward contracts determine the
exchange rate today for a fixed (not a floating) principal exchange from
deutsche marks to U.S. dollars in the future.
15. The reader should note that the important distinctions between diff
swaps and swaps with separable risks do not arise because the diff swap
involves a foreign currency. The risks of standard cross-currency swaps,
for example, can be valued and hedged separately.
9. If the market for providing these swaps is competitive, the buyer and
seller agree on an additional periodic payment, called “margin,” so that
the present value of the swap is zero at swap initiation.
The author would like to thank Karen Albano and Dan Schorr for assistance in
this study's early development. He also thanks Ladan Archin, Maria Mendez,
Rob Reider, and Asani Sarkar for helpful suggestions.
NOTES
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
19
REFERENCES
Bank for International Settlements, Euro-currency Standing Committee. 1994.
“Discussion Paper on Public Disclosure of Market and Credit Risks by
Financial Intermediaries.” August.
Bank for International Settlements. 1995. “An Internal Model-Based
Approach to Market Risk Capital Requirements: Consultative
Proposal by the Basle Committee on Banking Supervision.”
Cookson, Richard. 1992. “Stock-Still.” RISK MAGAZINE, November: 27-33.
Das, Satyajit. 1992a. “Differential Strip-Down.” RISK MAGAZINE, June:
65-72.
_____. 1992b. “Differential Operators.” RISK MAGAZINE, July-August:
51-52.
Dravid, Ajay, Matt Richardson, and Tong-Sheng Sun. 1993. “Pricing
Foreign Index Contingent Claims: An Application to Nikkei Index
Warrants.” JOURNAL OF DERIVATIVES, fall: 33-51.
Elms, David. 1995. “Rationale of Ratios.” RISK MAGAZINE, August: 24-28.
20
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
Group of Thirty Global Derivatives Study Group. 1993. “Derivatives:
Principles and Practices.” Washington, D.C.
Jamshidian, Farshid. 1993. “Price Differentials.” RISK MAGAZINE, July:
48-51.
Locke, Jane. 1995. “Relative Values.” RISK MAGAZINE, August: 21-22.
Remolona, Eli. 1992-93. “The Recent Growth of Financial Derivatives
Markets.” FEDERAL RESERVE BANK OF NEW YORK QUARTERLY
REVIEW 17, no. 4: 26-43.
Shirreff, David. 1992. “Noises from the Hedge.” RISK MAGAZINE,
November: 21-24.
Thomas, Sam. 1994. “The Plight of the First Stock Index Futures
Contract: Was It a Case of the Market Using the Wrong Model and
Not Learning?” Case Western Reserve University working paper.
Wei, Jason. 1994. “Valuing Differential Swaps.” JOURNAL OF
DERIVATIVES, spring: 64-76.
NOTES
The Commodity–Consumer Price
Connection: Fact or Fable?
S. Brock Blomberg and Ethan S. Harris
The views expressed in this article are those of the authors and do not necessarily reflect the position of the Federal
Reserve Bank of New York or the Federal Reserve System.
The Federal Reserve Bank of New York provides no warranty, express or implied, as to the accuracy, timeliness, completeness, merchantability, or fitness for any particular purpose of any information contained in documents produced
and provided by the Federal Reserve Bank of New York in any form or manner whatsoever.
I
nterest in commodity prices as indicators of consumer price inflation has ebbed and flowed with
the rise and fall in commodity prices themselves.
True to form, as commodity prices have surged in
the last two years (Chart 1), interest in their predictive
power has returned. Inflation hawks point to an outpouring of studies in the late 1980s showing a strong empirical
connection between commodity prices and subsequent
consumer inflation. Indeed, the concern over commodities
has grown to the point where even two previously obscure
commodity indexes—the National Association of Purchasing Managers price index (NAPM) and the Federal Reserve
Bank of Philadelphia’s prices paid index (PHIL)—have
begun to capture considerable attention among economists
and market analysts.
Is this renewed attention warranted? In this article, we argue that none of the channels through which
commodity prices signal more generalized inflation are
operating as well as they did in the past: commodities have
become less important as an input to production, some of
the inflation signals from commodity prices may be sterilized by offsetting monetary policy, and commodities have
become less popular as an inflation hedge. We also present
evidence that the recent commodity movements are a reaction to swings in dollar exchange rates rather than a signal
of generalized inflation pressures.
Our empirical results underscore the diminished
signaling power of commodities in the last eight years.
Drawing on data for the 1970-94 period, we examine five
major U.S. commodity indexes and three subgroups of
commodities—gold, oil, and food. We use vector autoregression models (VARs) to test whether commodity prices
are useful in predicting subsequent movements in both the
finished goods producer price index (PPI) and the core—
that is, nonfood and nonenergy—consumer price index
(CPI). These VAR methods allow us to isolate the predictive power of commodity prices while controlling for other
determinants of inflation. We find that:
• Contrary to conventional theory, there is no long-run
link between the level of commodity prices and the
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
21
level of consumer prices, but there is a link—or cointegrating relationship—between the level of commodity
prices and the rate of consumer price inflation.
• During the full 1970-94 sample period, all of the traditional commodity indexes have some ability to predict short-run changes in core CPI inflation.
However, this relationship weakens considerably
starting in the mid-1980s. The breakdown extends
beyond commodity prices: even the finished goods
PPI cannot help predict changes in core CPI inflation
in the recent period.
• Adding monetary variables and the dollar exchange
rate to the models helps eliminate some perverse findings, suggesting that some inflation signals from
commodities are being obscured by offsetting changes
in exchange rates and monetary policy.
• Commodities that are particularly sensitive to major
supply disruptions (such as food and oil) appear to
have retained more explanatory power than those
influenced primarily by input demands (industrial
materials) or those used for inflation hedging (gold).
Chart 1
Recent Commodity Price Movements
Index: November 1993 = 100
150
PHIL
140
NAPM
130
120
JOC
110
CRB
100
Crude PPI
90
N D J
1993
F M A M J J
1994
A
S
O N D
J
F M A M
1995
,
Sources: Authors calculations, based on data from Bureau of Labor Statistics,
Commodity Research Bureau, Journal of Commerce, National Association
of Purchasing Managers, Federal Reserve Bank of Philadelphia.
Note: Chart shows the price movements tracked by five major commodity
indexes. PHIL is the Federal Reserve Bank of Philadelphia’s prices paid index;
NAPM, the National Association of Purchasing Managers price index; JOC,
the Journal of Commerce index; CRB, the Commodity Research Bureau index;
crude PPI, the crude producer price index.
22
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
Our examination of the signaling power of commodities begins with a review of the theoretical linkages
between commodity prices and subsequent consumer price
inflation.
THE TORTOISE AND THE HARE AND OTHER
COMMODITY FABLES
Most arguments for a signaling role for commodities rest
on the fact that commodity prices are set in auction or
flexi-price markets and therefore can sprint ahead quickly
in response to actual or expected changes in supply or
demand. By contrast, prices of most final goods and services, restrained by contractual arrangements and other
frictions, respond slowly and steadily to supply and
demand pressures, only gradually gaining ground on commodity prices. Like the hare in Aesop’s famous fable, commodity prices tend to take a quick, early lead in inflation
cycles, but ultimately lose the race, falling in real terms.
Formal theoretical models, such as Boughton and
Branson (1991) and Fuhrer and Moore (1992), are based on
this notion of commodity behavior, building on Dornbusch’s (1976) classic exchange rate model. In these models, commodities are assets whose price “jumps” to
equilibrate the money and goods markets. Thus, a surge in
aggregate demand (for example, an unexpected increase in
the money supply) causes commodity prices to shoot
upward while final goods prices respond only with a lag.1
The empirical literature on commodities expands
on this simple theoretical framework and presents three
different accounts of the linkages between commodity
prices and broad inflation. These accounts—or commodity
“fables”—explain why commodity prices could be a useful
leading indicator of inflation.
First, as illustrated by the tortoise-and-hare fable,
commodity prices may give early warning signals of an
inflationary surge in aggregate demand. Higher demand
for final goods increases the demand for commodity inputs
and, even though the inflation impetus may start in final
goods markets, the first visible increase in prices may be in
the flexi-price commodity markets.2 Because commodities
are widely traded internationally, this aggregate demand
signal would most likely occur when strong domestic
demand is not offset by weak foreign demand. Indeed, in
empirical models, commodity prices are often modeled as a
function of global economic activity. These demandinduced commodity price run-ups presumably will be concentrated in industrial materials.
Second, commodity prices and broad inflation may
be directly linked because commodities are an important
input into production, representing about one-tenth of the
value of output in the United States. Thus, all else being
equal, an increase in commodity prices should eventually
be passed through to final goods prices. Historically, large
direct input price effects have tended to be concentrated in
food and energy commodities.
The third linkage between commodity prices and
future inflation stems from the first two. Because commodity prices respond quickly to general inflation pressures,
Like the hare in Aesop’s famous fable,
a very active literature in the late 1980s established the
following:3
• Although commodity prices and consumer prices
tend to diverge over time, commodity price levels and
consumer price inflation tend to move together over
time—that is, they are cointegrated (Boughton and
Branson 1991; Cody and Mills 1991).
• Commodities have significant predictive power in
explaining short-run movements in CPI inflation,
even when researchers control for information contained in monetary aggregates, real output, interest
rates, and exchange rates (Horrigan 1986; Webb
1988; Durand and Blondal 1991; Cody and Mills
1991; Garner 1989).
• The economic magnitude of these signals, however,
may be small (Horrigan 1986; Furlong 1989; Garner
1989).
• There is some evidence that these relationships have
shifted over time, with stronger linkages in the late
1970s and early 1980s than in the earlier period
(Whitt 1988; Furlong 1989).
commodity prices tend to take a quick, early
lead in inflation cycles, but ultimately lose
the race, falling in real terms.
investors may see them as a useful inflation hedge. This
perception tends to be self-fulfilling: the more that commodities are seen as an effective hedge, the more likely
investors are to turn to them in anticipation of inflation.
Traditionally, precious metals have been singled out as the
most convenient commodities for hedging inflation.
VAR LITERATURE
These three fables motivate empirical studies of the commodity–consumer price connection. Most studies, however, avoid the complications of a formal structural model
and instead use VAR models to test for a positive correlation between commodity prices and subsequent consumer
price inflation. The VAR methodology assumes that each
variable can be best explained by using past values of both
itself and all other relevant variables. Using this approach,
Despite the empirical consensus, there are reasons
to believe that the commodity-CPI connection may have
weakened since the mid-1980s. First, with commodities
playing a smaller role in U.S. production, and in the
absence of major food and oil price shocks, recent commodity price fluctuations may not have been big enough to be
passed through to consumer prices. Second, the theoretical
literature on commodity prices suggests that the recent
attention of monetary authorities to commodity prices may
have diminished commodities’ signaling role.4 This would
occur if monetary authorities eased or tightened policy in
response to the inflationary signals of commodity prices
and thereby mitigated the actual inflation outcome. Third,
because commodity investments have yielded a poor return
in recent years, they have lost some appeal as inflation
hedges, making them less sensitive to inflation expectations. Finally, recent commodity movements may have little to do with underlying inflation pressures and instead
may reflect a rebound in very depressed markets and the
impact of movements in dollar exchange rates.
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
23
TESTING COMMODITIES’ SIGNALING POWER
EIGHT COMMODITY PRICE INDICATORS
For our empirical tests, rather than focus on a single commodity index, we consider five popular alternative indexes
and three key subgroups of commodities. Each of the
indexes has advantages and disadvantages relating to the
properties of its construction and its correspondence to the
various commodity fables.
The most popular indicators in past empirical
research have been the Commodity Research Bureau (CRB)
spot index, the Journal of Commerce (JOC) index, and the
crude PPI:
• The CRB index is a simple, equally weighted average
of twenty-three commodities, including foodstuffs
and industrial materials. It is updated instantly on
computer screens and is the most closely watched
commodity index.
• The JOC focuses just on industrial commodities and
is therefore presumably well suited to capture the
tortoise-and-hare fable discussed above. It also has the
advantage of being specifically weighted according to
the inflation sensitivity of each of its components.
• The crude PPI is divided about evenly into three
parts: food, energy, and other. It is weighted according to the actual value of commodity shipments and
therefore presumably is the best index for exploring
how commodity price increases are passed through to
final goods prices.
In addition to these three traditional indexes, two
survey-based measures of commodity prices have recently
garnered attention—the NAPM and PHIL price indexes.
Both of these indexes measure the diffusion of price
increases across firms:
• The NAPM index measures the percentage of manufacturing firms reporting higher material prices, plus
half the percentage of those firms reporting no change
in prices. It therefore has a value of roughly 50 percent when aggregate prices are unchanged.
• The PHIL index, calculated a bit differently, is the
percentage of firms in the Philadelphia region reporting higher prices, minus the percentage reporting
lower prices; hence, it should have a value of roughly
zero when aggregate prices are unchanged.
24
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
Historically, both of these diffusion indexes have proved to
be quite sensitive to conditions in commodity markets.
Three subgroups of commodities are also potentially useful inflation predictors:
• Gold traditionally has been the commodity most
associated with inflation hedging.
• Food and oil have both been subject to major supply
disruptions and can be used to pinpoint the price
pass-through scenario.
IMPRESSIONISTIC EVIDENCE: TURNING POINTS
AND TRENDS
The simplest, least technical test of the inflation-signaling
power of commodity prices is to look at turning points in
the inflation cycle. The top panel of Chart 2 plots core CPI
inflation, with shading to indicate periods of falling inflation; the bottom panel plots inflation in the JOC index and
superimposes the shaded regions from the core CPI chart.
The chart illustrates why commodity prices gained popularity as inflation indicators in the 1970s: from the late
During the 1960s and over the last decade, the
JOC index has been a poor leading indicator of
turning points in inflation, sending more false
signals than correct signals.
1960s to the early 1980s, JOC inflation peaks and troughs
regularly predated peaks and troughs in core CPI inflation.
There were no missing signals over this period and there
was only one false signal: in 1976, JOC inflation peaked
and then declined, but CPI inflation continued to trend up.
Chart 2 also underscores why we suspect that commodity prices have not always been reliable indicators of
future inflation. During the 1960s and over the last decade,
the JOC index has been a poor leading indicator of turning
points in inflation, sending more false signals than correct
signals. For the most recent period, strong false signals
have occurred in 1987 and 1992. Even the correct signals
have been somewhat misleading, with very sharp commod-
ity price surges preceding relatively mild inflation accelerations. Similar results hold for the other major commodity
indexes. Thus, on a stand-alone basis, commodity price
indexes appear to be relatively unreliable indicators of
inflation in the recent period.
Another reason to suspect a breakdown in the
commodity-CPI connection is the steady drifting apart of
price levels. Chart 3 plots three stages of producer prices—
the crude, intermediate, and finished goods PPIs—along
with the core CPI since 1967. Note that each stage seems
to be relatively tightly linked until 1980. After that, each
index seems to drift apart, with the magnitude of the drift
increasing at each stage of fabrication. Although this drift
does not necessarily compromise the short-run commodity-
Chart 2
The JOC Index and Turning Points in Inflation
Percent
14
Core CPI Inflation
12
10
8
6
4
2
0
40
JOC Index
30
20
10
0
-10
-20
1958
60
65
70
75
80
85
90
7/95
Sources: Bureau of Labor Statistics; Journal of Commerce.
Notes: Each series is a three-month moving average of twelve-month percentage changes. The shaded areas denote periods of declining core CPI inflation.
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
25
Chart 3
Prices by Stage of Fabrication
Index: January 1980 = 100
250
Core CPI
200
Finished PPI
150
100
Crude CPI
Intermediate PPI
50
0
1967
70
75
80
85
90
6/95
Source: Bureau of Labor Statistics.
CPI relationship, it does make the arguments for a longrun price pass-through more tenuous.
FORMAL TESTS: VARS
The impressionistic evidence suggests that the linkage may
have broken down; we now present more rigorous evidence
of a structural shift. We assess the overall performance of
the commodity indicators using conventional VARs, which
provide simple tests of the short-run causal relationship
between these variables. In addition to using conventional
VARs, we present in the appendix the results obtained by
using two alternative VAR models: error correction models, which test for long-run as well as short-run linkages;
and time-varying parameter models, which can be used to
explore shifts in the relationships among the variables
without having to divide the sample. These alternative
models generally confirm the findings for the conventional
VARs.
For our VAR tests, we regress core CPI on lags of
itself and lags of a commodity index. Each equation also
includes a constant, a time trend, and the prime-age male
unemployment rate to control for business cycle impacts
on inflation. All variables included in the models are
appropriately differenced to ensure that the data are “sta-
26
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
tionary”; we also include twelve lags on each explanatory
variable.5 In addition to estimating our core CPI equations,
we test for a two-stage link between commodity prices and
core CPI inflation by first estimating the relationship
between the commodity indexes and the finished goods
PPI and then testing the impact of the finished goods PPI
on core CPI inflation. This two-stage approach enables us
to explore the commodity-CPI connection in more detail.
The results for the full sample6—January 1970 to
April 1994—confirm some findings in the literature. The
top panel of Table 1 shows tests of the joint statistical significance of twelve lags of the commodity indicators in predicting the change in core CPI inflation, as well as the sign
of the sum of the coefficients. The bottom panel of the
table shows the results when finished goods PPI inflation is
the dependent variable. If the commodity indexes are useful predictors of final goods inflation, we would expect the
sum of the coefficients to be positive and statistically significant (generally with p-values of less than .05). As in past
studies, the CRB and JOC indexes are significant and have
the correct sign in explaining both the core CPI and the
finished goods PPI. Thus, they seem to provide information
beyond that contained in the model’s other variables.
Some of the full sample results, however, are sur-
prising. The crude PPI is insignificant not only in the core
CPI equation, but in the finished goods PPI equation as
well. This result is particularly troubling for the price passthrough view of the inflation process because the crude
PPI—more than any other commodity index—is weighted
to reflect the use of commodities in production. Our finding also contradicts studies such as Horrigan’s (1986),
which found that the crude PPI was significant in explaining the first difference of CPI inflation for the 1959-84
period. The finished goods PPI does help explain core CPI
inflation, so there is only one weak link in the chain running from crude producer goods to finished producer goods
to consumer prices.
The results for the diffusion indexes—NAPM and
PHIL—also warrant some discussion since these indexes
have garnered considerable attention among business economists and financial market analysts but have been largely
ignored in the academic literature. These indexes have
advantages and disadvantages relative to the JOC and CRB
indexes. On the plus side, they reflect the actual prices
companies pay for inputs—through long-term contracts
and auction markets—whereas the CRB and JOC indexes
include only auction prices. On the minus side, they are
based on qualitative surveys and are not released to the
public until weeks after the data are collected (by contrast,
the JOC and CRB indexes are immediately available).7
Thus, it is an empirical question whether the release of
these diffusion indexes each month adds any information
beyond that already reported in the market-based indexes.
The full-sample findings in Table 1 suggest that the academics have been right to ignore the diffusion indexes: neither is useful in predicting either core CPI inflation or
finished goods PPI. Indeed, in “horse races”—when the
diffusion indexes enter in the same regression as either the
JOC or CRB index—they are never significant.
to April 1994). Preliminary tests showed a significant
structural break in these models in the mid-1980s, with
the qualitative results insensitive to the particular date
chosen.8 The results for the earlier sample continue to support previous research: the sum of the coefficients for the
commodity variables always has the correct sign and is
highly statistically significant. In contrast to the full sample results and in conformity with Horrigan (1986), the
crude PPI is also significant.
For the more recent period, the good news is that
all of the commodity indexes except CRB have a significant
positive relationship to the finished goods PPI. Indeed, in
contrast to the full sample, the two diffusion indexes—
NAPM and PHIL—have a significantly positive relationship with the finished goods PPI. The bad news, and perhaps this article’s key finding, is that except for the JOC
index, all of the commodity indexes have a perverse negative
relationship to core CPI inflation. Even the finished goods
PPI has developed a negative link, suggesting a breakdown
in the relationship between the inflation process in the
SPLIT SAMPLE RESULTS: A BREAK IN THE
COMMODITY-CPI CONNECTION
Sources: Authors’ calculations, based on data from Bureau of Labor Statistics,
Journal of Commerce, Commodity Research Bureau, National Association of Purchasing Managers, Federal Reserve Bank of Philadelphia.
Table 1 also shows the results when we split the sample
into two parts: an early period (January 1970 to December
1986), which roughly covers the period tested in many
previous studies, and the more recent period (January 1987
Notes: Table reports the sign and joint statistical significance of the coefficients
for each commodity index. The explanatory variables in the regression include a
constant, a linear time trend, and one to twelve lags of: the prime-age male
unemployment rate, the dependent variable, and a commodity index. NAPM
and the unemployment rate enter as levels; PHIL enters as a difference; and the
CRB, JOC, crude PPI, and finished PPI enter as log differences.
Table 1
VAR TESTS OF COMMODITIES AS INFLATION PREDICTORS
Dependent Variable: Change in Core CPI Inflation
1970-94
Commodity Indicator Sign P-Value
JOC
(+)
.01
CRB
(+)
.01
PPI crude
(+)
.32
NAPM
(+)
.20
PHIL
(+)
.52
PPI finished
(+)
.00
1970-86
Sign P-Value
(+)
.01
(+)
.00
(+)
.06
(+)
.00
(+)
.04
(+)
.00
1987-94
Sign P-Value
(+)
.06
(-)
.02
(-)
.04
(-)
.03
(-)
.01
(-)
.01
Dependent Variable: Finished Goods PPI Inflation
1970-94
Commodity Indicator Sign P-Value
JOC
(+)
.00
CRB
(+)
.01
PPI crude
(+)
.61
NAPM
(+)
.24
PHIL
(+)
.23
1970-86
Sign P-Value
(+)
.00
(+)
.00
(+)
.00
(+)
.00
(+)
.03
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
1987-94
Sign P-Value
(+)
.00
(+)
.57
(+)
.07
(+)
.00
(+)
.00
27
manufacturing sector and the overall economy. In other
words, for many indexes, an increase in commodity inflation has become associated with a future slowing in core
CPI inflation.9
OUT-OF-SAMPLE FORECASTS
Although Table 1 suggests that commodity indexes have
failed to correctly signal movements in core CPI inflation
in the recent period, it tells us nothing about the magnitude of this signaling error. To get a sense of the size of this
error, we take the parameter estimates for the 1970-86
period for the CRB and JOC models and simulate the
models dynamically over the 1987-94 period (Chart 4).
The out-of-sample errors from this forecasting exercise
could reflect either shifts in the coefficients for the commodity variables or shifts in other relationships in the
model. To pinpoint the impact of the weakened commodity connection, therefore, the chart presents three simulations: one excluding the commodity indexes, a second
including the CRB index, and a third including the JOC
index. The difference between the simulations with and
without the commodity indexes is used to measure the
additional error (or improvement) in the forecast due to the
commodity variable.
The simulations confirm that these models have a
chronic tendency to overestimate the change in inflation in
the recent period. This overprediction is due in part to
misleading signals from the commodity indexes and in
part to a shift in other relationships in the model. Chart 4
plots a twelve-month moving sum of the monthly forecast
errors. It shows that the model without a commodity index
predicted an earlier and more virulent acceleration in inflation in the 1987-89 period than in fact occurred; the
model also suggested an uptick in inflation in 1994 rather
than the actual downtrend. When the CRB index is
included in the model, the overpredictions are even larger,
The bad news, and perhaps this article’s key
finding, is that except for the JOC index, all of
the commodity indexes have a perverse negative
relationship to core CPI inflation.
Chart 4
particularly for 1989 and 1994, and the average annual
error is about 1 percentage point over the entire 1987-94
period.
The results are more dramatic for the JOC index:
the model significantly overpredicts over the entire period,
with annual errors of more than 2 percentage points in the
late 1980s and about 1 1/2 percentage points in 1994. This
poor performance is particularly troubling because this
index was designed specifically as an indicator of broad
inflation. Moreover, similar results are obtained when the
other commodity indexes are used, with an average annual
overprediction of about 1 percentage point.
Model Prediction Errors Attributable
to Commodity Indexes
Percent
3
Error with JOC
Error
with
CRB
2
1
0
Error without
CRB or JOC
-1
-2
1988
90
89
91
92
93
94
95
Sources: Authors calculations, based on data from Bureau of Labor Statistics,
Journal of Commerce, Commodity Research Bureau.
ARE THE PARTS WORTH MORE THAN
THE WHOLE?
Notes: The model for the acceleration in core CPI inflation is estimated
through December 1986 and then dynamically simulated forward. The
forecast prediction errors are reported as a twelve-month moving sum.
The sums for the first twelve months include both in-sample and
out-of-sample errors.
By lumping together a diverse group of commodities, the
indexes could obscure their components’ predictive power.
This would be the case if some commodities were not good
,
28
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
inflation predictors or if the timing of the inflation signals
varied among different kinds of commodities.
To investigate these possibilities, we subject three
narrowly defined commodities—gold, food, and oil—to
the same tests as the broader indexes (Table 2). Despite its
reputation as an inflation hedge, gold shows the weakest
results, sending unreliable signals for the full sample
period and both subsamples. Indeed, in the earlier period,
the sum of the coefficients on gold is negative and statistically significant, suggesting that rising gold prices are a
signal of falling consumer price inflation.
By contrast, both oil and food—with positive, significant coefficients—appear to be good predictors of core
CPI inflation in the earlier period. This is consistent with
the idea that major supply disruptions in these markets fed
through to general inflation in the 1970s and early 1980s.
In the more recent period, both continue to have the correct sign. In the case of oil, this probably reflects the
impact of the 1990 supply shock to oil prices. As we will
explain later, one reason for this positive response may be
that monetary policymakers are more reluctant to tighten
when the commodity price rise is due to a supply shock
rather than a demand shock. Supply shocks pose a dilemma
for policymakers because inflation pressures increase at the
same time that real economic activity weakens. Hence,
supply-induced increases in commodity prices are more
likely to be allowed to show through to increases in final
goods prices.
EXPLAINING THE DIMINISHED SIGNALING
POWER OF COMMODITIES
Commodity prices have clearly become a much less reliable
indicator in the recent period. In this section, we combine
impressionistic evidence, results from other research, and
our own empirical findings to support three explanations
for the shift:
• the diminished use of commodities as inflation
hedges,
• monetary policy reactions to commodity prices, and
• the shift away from commodity-intensive production.
In recent years, commodities have lost much of
their reputation as an effective tool for hedging inflation.
Over the postwar period, all three major commodity
indexes have failed to keep up with inflation and have been
particularly poor performers during the last twenty years
(Table 3). Some individual commodities have fared better
but have still fallen well short of safer investments, such as
Treasury bonds. For example, although gold prices have
matched the CPI for the 1975-94 period as a whole, they
have been a very volatile investment, skyrocketing in the
late 1970s, then dropping sharply, and finally hovering
around $400 per ounce for more than a decade. It is therefore not surprising that investors have generally rejected
commodities as an inflation hedge and instead are using
financial futures on interest rates or exchange rates. For
Table 3
THE ANNUAL
Table 2
THE PREDICTIVE
POWER OF THREE COMMODITY SUBGROUPS
1970-94
Commodity Indicator Sign P-Value
Gold
(-)
.31
Food
(+)
.01
Oil
(+)
.05
1970-86
Sign P-Value
(-)
.05
(+)
.00
(+)
.01
1987-94
Sign P-Value
(-)
.18
(+)
.00
(+)
.02
Sources: Authors’ calculations, based on data from Bureau of Labor Statistics,
Journal of Commerce, Commodity Research Bureau.
Notes: The dependent variable is the second difference of log core CPI. The table
reports the sign and joint statistical significance of the coefficients for each commodity index. The explanatory variables in the regression include a constant, a
linear time trend, and one to twelve lags of: the prime-age male unemployment
rate; the dependent variable; and the price index for either gold, food (a subcomponent of the CRB), or oil (West Texas Intermediate posted price before 1982
and spot price thereafter). The unemployment rate enters as a level, gold enters as
a log difference, and oil and food enter as second log differences.
REAL RETURN TO COMMODITIES
Commodity Indicator
JOC
CRB
PPI crude
Gold
Nonferrous metals
Food and feed
Oil
Memo: Ten-year Treasury bonds
Postwar
-2.4
-1.4
-1.2
1975-94
-3.1
-3.0
-1.8
1.4
0.0
-1.8
1.1
0.1
-1.0
-2.8
-2.2
2.6
3.5
Sources: Authors’ calculations, based on data from Bureau of Labor Statistics,
Journal of Commerce, Commodity Research Bureau, Board of Governors of
the Federal Reserve System.
Notes: Each variable is deflated by the CPI. The postwar sample starts in 1947,
except for JOC and CRB, which start in 1948 and 1967, respectively. Nonferrous
metals and food and feed are components of the crude producer price index, and oil
is the West Texas Intermediate posted price before 1982 and spot price thereafter.
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
29
instance, in 1993, trading in Treasury bond futures outnumbered trading in gold futures more than ten to one
(Einhorn 1994). If gold and other commodities are not
seen as reliable inflation hedges, then less of their movement will be due to changes in inflation expectations (and
a larger portion will be due to factors specific to commodity markets).
A second explanation for the weaker predictive
power of commodities is that they may be an example of
Goodhart’s law. Goodhart argued that “any statistical regularity will tend to collapse once pressure is placed on it for
control purposes.” Therefore, if investors believe that monetary authorities are reacting to the inflation signals from
commodity prices, then the commodity price movements
will begin to reflect market expectations of monetary policy rather than independent information on the economy.
As an extreme example, Fuhrer and Moore (1992) show
that if the monetary authorities include commodities in
their “reaction function,” even “mild targeting pressure”
on commodity prices can lead to perverse results, with
increases in commodity prices predicting a decline in final
goods prices. In this case, the signal of incipient inflation
pressures from commodities may be correct, but little
actual inflation occurs because of offsetting monetary pol-
Table 4
COMMODITY COEFFICIENTS WHEN MONEY AND THE
ARE ADDED TO THE 1987-94 MODEL
Commodity Indicator
JOC
CRB
PPI crude
NAPM
PHIL
PPI finished
Core CPI Model
Sign
P-Value
(+)
.00
(+)
.00
(+)
.00
(-)
.00
(-)
.03
(+)
.00
DOLLAR
Finished PPI Model
Sign
P-Value
(+)
.00
(+)
.00
(+)
.00
(+)
.00
(-)
.00
NA
NA
Sources: Authors’ calculations, based on data from Bureau of Labor Statistics,
Journal of Commerce, Commodity Research Bureau, National Association of Purchasing Managers, Federal Reserve Bank of Philadelphia, Board of Governors of
the Federal Reserve System.
Notes: Table reports the sign and joint statistical significance of the coefficients for
each commodity index. The explanatory variables in the regression include a constant, a linear time trend, and one to twelve lags of: M2, the trade-weighted dollar
(Board of Governors of the Federal Reserve System measure), the prime-age male
unemployment rate, the dependent variable, and a commodity index. NAPM and
the unemployment rate enter as levels; PHIL enters as a difference; and M2, the
dollar, CRB, JOC, crude PPI, and finished PPI enter as log differences.
30
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
icy. To continue our tortoise-and-hare analogy: the hare
sprints ahead, but the authorities cancel the race before it
heats up.
To test whether monetary policy may have offset
some inflation signals from commodity prices, we added a
variety of monetary policy measures to our VAR model for
the 1987-94 period. Table 4 shows the typical results
when M2 and the dollar are added: controlling for monetary policy in this way causes the coefficients to switch
signs from negative to positive for several commodity variables.10 This finding suggests that some of the weakening
in the commodity-inflation connection stems from policy
reaction.
As Chart 5 shows, however, adding M2 and the
dollar only partly solves the tendency of these models to
overpredict the acceleration in inflation in the recent
period. In particular, we repeat the out-of-sample exercise
reported earlier, estimating the JOC and CRB models over
the 1970-86 period and then simulating them over the
recent period. Adding M2 and the dollar to each model
does reduce the twelve-month sum of these out-of-sample
forecast errors by an average of about 0.2 percentage
points, but large overpredictions remain.11
These results complement the literature on the
“price puzzle.” Christiano, Eichenbaum, and Evans (1994)
and others have pointed out that in a simple VAR framework, money tends to have a perverse relationship to
aggregate prices—a tightening of policy raises the price
level. They also note that if a commodity indicator is added
to the model, the price puzzle tends to go away. Here we
have turned this puzzle around and have shown that in the
recent period, commodities have had a perverse link to
aggregate prices—higher commodity prices predict a
decline in final goods prices—but the puzzle is partially
solved by including money in the model.
The final—and probably most important—factor
in the diminished commodity-CPI connection is the sharp
decline in the commodity composition of U.S. output.
According to Rosine (1987), consumption of spot commodities as a share of nominal GDP ranged from 8 percent
to 10 percent from 1973 to 1981, but fell to just 4 percent
by 1986.12 With the ongoing technological revolution, this
decline has presumably continued into the 1990s.
This diminished role seems to reflect a sharp
downward shift in demand for commodities that has lowered both the relative price of commodities and the growth
in quantity consumed. Final demand has moved steadily
away from goods with high commodity content (such as
food, textiles, and furniture) toward sectors with low commodity content (such as engineering products, electronics,
plastics, and services). For example, from 1948 to 1994,
the share of services in consumer spending almost doubled,
from 32 percent to 57 percent. Furthermore, although
commodity price inflation has exceeded CPI inflation for
brief periods, for the 1970-94 period as a whole, commodities have lost more than half their value relative to consumer prices (Chart 6). This reduced role for commodities
means that they are a less reliable inflation signal, not only
The final—and probably most important—
Chart 5
factor in the diminished commodity-CPI
Prediction Errors in a Model That Controls
for Monetary Policy
connection is the sharp decline in the commodity
Percent
3
Model with CRB, M2, and the Dollar
composition of U.S. output.
2
Error with CRB
because price pass-through effects are weakened, but
because as increasing parts of the economy become independent of commodity markets, a rise in commodity prices
is more likely to reflect an increase in a narrow part of final
demand than an increase in economy-wide demand.
1
0
Error with CRB, M2,
and the dollar
-1
WHY HAVE COMMODITY PRICES RISEN?
-2
4
Model with JOC, M2, and the Dollar
3
Error with JOC
2
1
Error with JOC, M2,
and the dollar
0
-1
-2
1988
89
90
91
92
93
94
,
95
Sources: Authors calculations, based on data from Bureau of Labor Statistics,
Journal of Commerce, Commodity Research Bureau.
Notes: The model for the acceleration in core CPI inflation is estimated
through December 1986 and then dynamically simulated forward. The
forecast prediction errors are reported as a twelve-month moving sum.
The average for the first twelve months includes both in-sample and
out-of-sample errors.
If commodities are not signaling major inflation pressures,
why have they risen so sharply? In large part, two factors
seem to be at work. First, in many cases, prices have
rebounded from unusually depressed levels. As in most
cycles, the initial rebound in commodity prices may represent a catching-up process or a return to more normal
input demands rather than a signal of economy-wide
capacity pressures. As Chart 6 shows, even with their
recent rebound, commodity prices remain well below their
late 1980s peaks in real, CPI-adjusted terms.
Second, commodity prices may also have risen in
response to the weak dollar. We would expect commodities—which are homogenous goods and are heavily traded
in international markets—to be subject to the law of one
price, that is, to have similar prices in each country’s home
currency. Thus, if the dollar weakens relative to other currencies, all else being equal, commodity consumers outside
the United States should be willing to pay more dollars for
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
31
Chart 6
“Real” JOC Index
Index: January 1980 = 100
160
140
120
100
80
60
40
1958
65
60
70
75
80
90
85
7/95
Sources: Bureau of Labor Statistics; Journal of Commerce.
Note: The values are calculated by dividing the JOC index by the CPI and then rescaling the data to equal 100 in January 1980.
Chart 7
Commodity Inflation and Dollar Appreciation
Twelve-month percentage change
50
Correlation with Dollar
1970-86 1987-94
-0.37
-0.02
JOC
-0.34
-0.19
CRB
-0.33
-0.17
PPIC
-0.64
NAPM -0.09
-0.40
0.15
PHIL
40
JOC index
30
20
10
0
-10
Trade-weighted
dollar
-20
-30
1967
70
75
80
Sources: Journal of Commerce; Board of Governors of the Federal Reserve System.
32
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
85
90
6/95
commodity inputs, bidding up their dollar price.13 Chart 7
shows that commodity prices have been particularly sensitive to dollar movements in recent years. For example, over
the 1971-86 period, the simple correlation between
twelve-month changes in the dollar and the JOC index was
only -0.02, but grew to -0.34 in the 1987-94 period.
CONCLUSION
This article has analyzed the short- and long-run relationships between commodity prices and consumer prices.
Using several VAR specifications, we find that most commodity indexes did have predictive power in explaining
consumer inflation in the 1970s and early 1980s. However,
we also present evidence that commodities have either lost
that power or, in some cases, are sending perversely negative signals.
What accounts for this poor performance? Commodities have declined in importance, both as a share of
final output and as a source of exogenous shocks to the
economy. Some commodity price signals may also have
been offset by countervailing changes in monetary policy.
Furthermore, much of the recent commodity price run-up
should be seen as both a reaction to the dollar’s weakness
and a normal catch-up from very depressed levels.
These findings clearly pour some cold water on the
use of commodities as inflation signals in the recent period.
But could commodities regain their predictive power in
the future? There is little reason to expect a change in the
trend away from commodity-intensive production; commodities should continue to diminish in importance as a
measure of input prices and as an indicator of broad-based
strength in the economy. In other respects, however, their
signaling power may partially revive. Commodities should
remain an indicator of global excess demand. Thus, even if
they do poorly in predicting inflation in individual countries, they should retain some role as global inflation predictors. There are also signs of a partial revival in
commodity investments as inflation hedges: several new
commodity funds cropped up in the last year.
Nonetheless, in the absence of a major supply
shock, commodity prices should remain a secondary indicator of future inflation. Inflation hawks might more profitably focus on the unemployment rate and other indicators
for signs of future inflation.
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
33
APPENDIX I: COMMON FORMS OF CORRELATION PRODUCTS
The conventional VAR methods reported in this article are
the most popular, but not the only, econometric methodology used in the commodity literature. This appendix
briefly reviews the results for two alternatives: (1) error correction VARs, which help us regain information on the
long-run relationships among the variables, and (2) timevarying parameter VARs, which provide a more flexible
test for shifts in the model relationships.
ERROR CORRECTION VARS
If two or more series have a cointegrating relationship—an
equilibrium relationship to which they gravitate over
time—then conventional VAR specifications ignore useful
information. Error correction VAR models can help us
regain information on these long-run relationships. In this
two-stage procedure, we first estimate a cointegrating vector and we then add the lagged errors from this cointegrating regression—the error correction term—to the
conventional VAR model to explain the acceleration in CPI
inflation.
The stationarity tests reported in this article limit
the scope for cointegration. Two series can only be cointegrated at one degree of differencing less than the differencing needed to achieve stationarity. As a result:
• NAPM, which is stationary in levels, cannot be cointegrated with the core CPI, and
• the other four commodity indexes and the core CPI
cannot be cointegrated at the same degree of differencing because the commodity indexes are stationary
in first differences, while the core CPI is stationary in
second differences.
Nonetheless, cointegration tests were run and revealed that
the levels of the JOC, CRB, and crude PPI indexes were
cointegrated with core CPI inflation, but only if the finished
goods PPI was also included in the cointegrating vector.14
These cointegration results present a dilemma for
34
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
the literature on the commodity–consumer price connection. Although the statistical results show a long-run linkage between the level of commodity prices and the rate of
core CPI inflation, this relationship is difficult to reconcile
with economic theory. For example, in a price pass-through
model, why would a onetime increase in the price of a commodity input cause a permanent increase in the rate of
growth in output prices? The puzzling nature of our findings prompted us to focus on the conventional VAR tests
of a short-run commodity-CPI linkage in this article.
With this important caveat in mind, we present in
the appendix table the error correction results for the three
DUMMY VARIABLE TESTS IN AN ERROR CORRECTION
VAR MODEL
Full Sample
Sign
P-Value
Dummy Variable
Sign
P-Value
CRB model
Error correction
CPI
Finished PPI
CRB
(-)
(+)
(+)
(+)
.01
.00
.01
.59
(+)
(-)
(-)
(-)
.72
.00
.00
.00
JOC model
Error correction
CPI
Finished PPI
JOC
(-)
(-)
(+)
(+)
.01
.00
.17
.34
(-)
(-)
(-)
(-)
.48
.00
.10
.33
Crude PPI model
Error correction
CPI
Finished PPI
Crude PPI
(-)
(-)
(+)
(-)
.00
.00
.00
.00
(+)
(-)
(-)
(+)
.09
.00
.00
.00
Sources: Authors’ calculations, based on data from Bureau of Labor Statistics,
Journal of Commerce, Commodity Research Bureau, National Association of Purchasing Managers, Federal Reserve Bank of Philadelphia.
Notes: Table reports the sign and joint statistical significance of the coefficients
for the principal explanatory variables and the corresponding dummy variables.
The dummy variables are set equal to the explanatory variables for the 1987-94
period, and are zero otherwise. The regression equation includes a constant, a linear time trend, the lagged errors from the cointegrating regression, and one to
twelve lags of: the prime-age male unemployment rate, the dependent variable,
the finished PPI, and a commodity index. NAPM and the unemployment rate
enter as levels; the CRB, JOC, crude PPI, and finished PPI enter as log differences.
APPENDIX II: ANALYZING THE PRICE RISK OF A STANDARD INSTRUMENT:
THE CONSTANT MATURITY TREASURY SWAP (Continued)
commodity models. The explanatory variables, including
the error correction term, are listed at the left. The first two
columns show the sign and the joint statistical significance
of the sum of the lagged coefficients associated with each
variable. The last two columns continue our tests for a
structural shift in these relationships, showing the sign and
statistical significance of dummy variables. These variables
take on a value equal to the explanatory variable for the
1987-94 period and are zero otherwise. The coefficients for
the dummy variables show whether the relationship has
shifted in the more recent period, becoming either stronger
(positive coefficient) or weaker. The formulation also allows
for a formal Chow test of whether the dummy variables are
jointly statistically different from zero.
The results from this more complicated model
generally support the VAR findings. In particular, the coefficients for the commodity price dummy variables provide
The Changing Link between the JOC and the CPI
Sum of coefficients
0.06
0.05
0.04
0.03
0.02
0.01
0
1957
60
65
70
75
80
85
90
94
,
Sources: Authors calculations, based on data from Bureau of Labor Statistics,
Journal of Commerce.
Notes: The chart is based on a regression of the second difference in the log
of core CPI on a constant, a linear time trend, the prime-age male unemployment rate, and one to twelve lags of the dependent variable and the log
change of the JOC index. All parameters are estimated assuming they follow
a random-walk process. The sum of the coefficients on the JOC index is
plotted as a twelve-month moving average to smooth out month-to-month
variations. The shaded area denotes the 1987-94 period.
APPENDIX
further evidence of a diminished short-run linkage between
commodities and core CPI inflation in the recent period.
The coefficients on both the CRB and JOC dummy variables are negative; for the CRB index, the shift is highly
statistically significant. Chow tests are highly significant in
all three cases, confirming a shift in the overall relationships in the model.
TIME-VARYING PARAMETER ESTIMATION
Using dummy variables or splitting the sample does not
allow us to examine the evolution of the coefficient estimates. In this section of the appendix, we allow the coefficients associated with commodity prices to vary over time.
This methodology is useful because it enables us to examine when the relationship between commodity prices and
inflation appears strongest and when it appears weakest.
The time-varying technique uses initial conditions to estimate coefficients and updates the coefficients under the
assumption that the parameters are persistent, that is, follow a random-walk process. The econometrics involved
closely resemble those used in Doan, Litterman, and Sims
(1984) and are briefly reviewed in Blomberg and Harris
(1995).
We estimate the time-varying model for all commodity indexes and obtain qualitatively similar results for
all indexes. Therefore, we report only those results for the
JOC because it has the longest history of our commodity
series. The appendix chart plots the twelve-month moving
average of the sum of coefficients associated with the JOC
index. The results are generally consistent with our earlier
findings: the commodity coefficients tend to increase in the
1970s but decline in the more recent period. The decline
appears modest because the estimation methodology only
gradually captures a structural shift; if the recent weaker
linkages continue, the time-varying coefficients will continue to fall as well.
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
35
ENDNOTES
1. Indeed, these models predict that commodity prices will tend to
overshoot the mark in response to a money supply increase, rising above
their long-run equilibrium initially and then falling back to
equilibrium.
2. Even if commodity prices rise simultaneously with final goods prices,
the increase will first be observed in commodity indexes because they are
updated almost immediately, while consumer price indexes are reported
with a lag of several weeks.
commodity price variables had the correct sign for the full sample, only
four of five were correct for the 1979-94 sample, only three were correct
for 1983-94, only two for 1985-94, only one for 1987-94, and none for
1989-94. We settled on the 1987 split not only to make comparisons
with previous research, but also to ensure an adequate number of
observations in each subsample.
9. In contrast to commodity prices, the prime-age male unemployment
rate remains a significant inflation predictor in all our equations,
regardless of the sample period.
3. For an excellent review of the literature, see Hilton (1990).
4. Starting in the late 1980s, several Federal Reserve Governors pointed
to a role for commodity prices in the conduct of policy. See, for example,
Angell (1987), Greenspan (1987), and Johnson (1988). Studies of the
Federal Reserve’s “reaction function” have found mixed evidence of a role
for commodity prices. Hakkio and Sellon (1994), for example, find that
commodity indexes are individually statistically significant in explaining
movements in the federal funds rate but do not add to the model’s overall
ability to predict the funds rate over the 1983-93 period.
5. If the data are not stationary—that is, if the underlying process that
generated the series changes over time—then classical tests are invalid.
Dickey-Fuller tests showed NAPM and the prime-age unemployment
rate to be stationary in levels; finished PPI, crude PPI, CRB, JOC, and
PHIL to be stationary in first differences; and the core CPI to be
stationary in second differences. We experimented with alternative lag
lengths. Akaike information criteria suggested that nine or twelve lags
were optimal for all our equations, with very little difference in the test
statistics. In keeping with the literature and to ensure that seasonal
effects were captured, we settled on twelve lags for all our tests. See
Blomberg and Harris (1995) for details of these tests.
6. Earlier data are available for some of our commodity indexes, but we
choose a uniform sample to make our tests comparable.
7. An additional disadvantage of the PHIL index as an indicator of
national inflation pressures is that it covers only a relatively narrow
geographic region.
10. Similar results were obtained using the federal funds rate as the
monetary indicator. For these equations, we also deleted the tradeweighted dollar, but this change did not materially affect the results for
the monetary variables.
11. The simulation results are sensitive to how the unemployment rate
enters the model. Although it is logical to assume that the
unemployment rate is stationary, the Dickey-Fuller tests suggest that we
may want to enter it in first differences rather than in levels. In this case,
although the commodity models still tend to strongly overpredict the
change in CPI inflation during periods of high commodity inflation, the
forecast errors for the 1987-94 period as a whole have less of an upward
bias. In addition, by including the change in the unemployment rate, we
reverse our finding for M2: it no longer appears to improve the out-ofsample forecast performance.
12. These figures understate total commodity consumption somewhat
because they include only purchases on spot markets.
13. A key assumption here is that the dollar movement is exogenous and
is causing the commodity price change. Alternatively, both the dollar
depreciation and the commodity price surge could reflect worsening
inflation expectations. It is hard to believe, however, that the relatively
modest inflation cycles of recent years could play much of a role in the
period’s dramatic exchange rate movements. It seems more plausible to
argue that swings in investor sentiment are driving the dollar, which in
turn is influencing commodity prices.
14. See Blomberg and Harris (1995) for formal test results.
8. In experimenting with alternative dates for splitting the sample, we
found a progressive deterioration in the commodity variable coefficients
as we moved through the 1980s. For example, although all of the
36
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
S. Brock Blomberg, formerly an economist at the Federal Reserve Bank of
New York, is currently an assistant professor of economics at Wellesley College.
NOTES
REFERENCES
Angell, Wayne D. 1987. “A Commodity Price Guide to Monetary
Aggregate Targeting.” Speech before the Lehrman Institute,
December 10, 1987.
Fuhrer, Jeff, and George Moore. 1992. “Monetary Policy Rules and the
Indicator Properties of Asset Prices.” JOURNAL OF MONETARY
ECONOMICS 29: 20-32.
Blomberg, S. Brock, and Ethan Harris. 1995. “Commodity Prices and CPI
Inflation.” Federal Reserve Bank of New York working paper.
Furlong, Frederick T. 1989. “Commodity Prices as a Guide for Monetary
Policy.” FEDERAL RESERVE BANK OF SAN FRANCISCO ECONOMIC
REVIEW, winter, no. 1: 21-38.
Boughton, James, and William H. Branson. 1991. “Commodity Prices as a
Leading Indicator of Inflation.” In K. Lahiri and G.H. Moore, eds.,
LEADING ECONOMIC INDICATORS: NEW APPROACHES AND
FORECASTING RECORDS, pp. 303-38. Cambridge: Cambridge
University Press.
Boughton, James, William H. Branson, and Alphecca Muttardy. 1988.
“Commodity Prices as a Leading Indicator of Inflation: Evidence from
Seven Large Industrial Countries.” National Bureau of Economic
Research Working Paper no. 3158.
Christiano, Lawrence J., Martin Eichenbaum, and Charles Evans. 1994. “The
Effects of Monetary Policy Shocks: Evidence from the Flow of Funds.”
Federal Reserve Bank of Chicago Working Paper Series no. 94-2.
Cody, Brian J., and Leonard O. Mills. 1991. “The Role of Commodity
Prices in Formulating Monetary Policy.” REVIEW OF ECONOMICS
AND STATISTICS 73, no. 2: 358-65.
Doan, T., R. Litterman, and C.A. Sims. 1984. “Forecasting and
Conditional Projection Using Realistic Prior Distributions.”
ECONOMETRIC REVIEWS 3: 1-100.
Dornbusch, Rudiger. 1976. “Expectations and Exchange Rate Dynamics.”
JOURNAL OF POLITICAL ECONOMY 84: 1161-76.
Durand, Martine, and Sveinbjorn Blondal. 1991. “Are Commodity Prices
Leading Indicators of OECD Prices?” In O. Guvenen, W. Labys, and
J. Baptiste, eds., INTERNATIONAL COMMODITY MARKET MODELS:
ADVANCES IN METHODOLOGY AND APPLICATIONS, pp. 305-21.
London: Chapman and Hall.
Garner, Alan C. 1989. “Commodity Prices: Policy Target or Information
Variable? A Note.” JOURNAL OF MONEY, CREDIT AND BANKING 21,
no. 4: 508-14.
Greenspan, Alan. 1987. Statement before the House Committee on
Banking, Finance, and Urban Affairs, December 18, 1987.
Hakkio, Craig S., and Mark Rush. 1991. “Co-integration: How Short is
the Long Run?” JOURNAL OF INTERNATIONAL MONEY AND FINANCE
10, no. 4: 571-81.
Hakkio, Craig S., and Gordon H. Sellon. 1994. “Monetary Policy Without
Intermediate Targets: Federal Reserve Policy since 1983.” Federal
Reserve Bank of Kansas City Research Working Paper no. 94-14.
Harris, Ethan S. 1991. “Tracking the Economy with the Purchasing
Managers’ Index.” FEDERAL RESERVE BANK OF NEW YORK
QUARTERLY REVIEW 16, no. 3: 61-69.
Hilton, Spence. 1990. “Commodity Prices as Intermediate Targets and
Information Variables.” In INTERMEDIATE TARGETS AND
INDICATORS FOR MONETARY POLICY, pp. 305-38. Federal Reserve
Bank of New York.
Horrigan, Brian R. 1986. “Monetary Indicators, Commodity Prices,
and Inflation.” Federal Reserve Bank of Philadelphia Working
Paper no. 86-7.
Johansen, Soren. 1991. “Estimation and Hypothesis Testing of Cointegration
Vectors in Gaussian Vector Autoregressive Models.” ECONOMETRICA 59,
no. 6: 1551-80.
Einhorn, Cheryl. 1994. “Gold Takes a Fade.” BARRON’S, April 25, 1994.
Englander, A. Steven. 1985. “Commodity Prices in the Current Recovery.”
FEDERAL RESERVE BANK OF NEW YORK QUARTERLY REVIEW 10,
no. 1: 11-19.
Engle, Robert F., and Mark W. Watson. 1985. “Applications of Kalman
Filtering in Econometrics.” Harvard Institute for Economic Research
Discussion Paper no. 1187.
NOTES
Johnson, Manuel H. 1988. “Current Perspectives on Monetary Policy.”
Speech before Cato Institute, February 25, 1988.
Rosine, John. 1987. “Aggregative Measures of Price and Quantity Change
in Commodity Markets.” Board of Governors of the Federal Reserve
System Working Paper no. 81.
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
37
REFERENCES (Continued)
Webb, Roy H. 1988. “Commodity Prices as Predictors of Aggregate Price
Change.” FEDERAL RESERVE BANK OF RICHMOND ECONOMIC
REVIEW 74, no. 6: 3-11.
Whitt, Joseph A. Jr. 1988. “Commodity Prices and Monetary Policy.”
Federal Reserve Bank of Atlanta Working Paper no. 88-8.
38
FRBNY ECONOMIC POLICY REVIEW / OCTOBER 1995
NOTES
@FedFRASER