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Business
Review
Federal Reserve Bank o f Philadelphia
January • February 1993

ISSN 0007-7011

Information Externalities
Why Lending May
Sometimes Need
a Jump Start
Leonard I. Nakamura

0IHSTC

Information



Business
Review
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JANUARY/FEBRUARY 1993

INFORMATION EXTERNALITIES:
WHY LENDING MAY SOMETIMES
NEED A JUMP START
Leonard 1. Nakamura
Information is essential to the efficient func­
tioning of credit markets. An information
externality occurs when the actions of one
person or firm influence the opportunities and
choices of another as a by-product. For ex­
ample, lenders rely on information generated
by the lending activities of other institutions.
But when this information is inaccurate, in­
complete, or unavailable, a bank may deny a
loan request. Leonard Nakamura offers ex­
planations as to why these externalities occur
and how they affect mortgage and commer­
cial lending decisions and notes some possible
remedies.
PREDICTING STOCK-MARKET
VOLATILITY
D. Keith Sill
Although the sharp drops of a 1929 type of
crash are, fortunately, rare in the stock market,
it isn’t uncommon for stock prices to rise or
fall by 3 percent or more in a single month.
The alternating turbulence and tranquility of
the stock market raises many questions: How
are stock prices determined? Why are stock
prices volatile? Can this volatility be pre­
dicted? How does this volatility affect the
economy? Keith Sill’s article presents some
answers to these questions.

FEDERAL RESERVE BANK OF PHILADELPHIA

Information Externalities:
Why Lending May Sometimes
Need a Jump Start
Leonard /. Nakamura*

A . banker, according to the comics, is some­

am

one who is willing to lend money to those who
can prove they don’t need it. As the joke
ruefully suggests, the work of a lender (whether
a banker or not) is to find someone who wants
money now and will be willing and able to
repay a larger sum in the future. From a
banker’s perspective, the first part of the re­
quirement is all too easy to fill; the second part
is the hard part. Bankers must compete to find

*Leonard Nakamura is a Senior Economist and Research
Adviser in the Philadelphia Fed’s Research Department.




and assess the good borrowers, and that puts
bankers into the information business: the prof­
itable lender is the one who best understands
the businesses that borrowers are engaged in
and the value of collateral that borrowers put
up to guarantee loans.
Information about borrowers and collateral
is thus essential to the flow of credit. But
although information is crucial to the efficient
operation of credit markets, it is often not itself
produced efficiently. In credit markets where
information flows are unsteady, private credit
institutions may need public assistance or prod­
ding.
3

BUSINESS REVIEW

Information externalities constitute one rea­
son credit markets are sometimes unreliable.
An information externality in credit markets
exists when each lender relies on information
generated by the lending activities of other
institutions.
An information externality can
cause a slowdown in lending activity to be selfperpetuating because the slowdown results in
a shortage of information available to lenders.
One example arises in the mortgage market.
A key informational need in a mortgage loan is
an accurate measure of the value of the house
that serves as collateral to guarantee the loan.
The accuracy of appraisals is reduced when
there are fewer recent sales. Where mortgage
lenders cannot accurately evaluate collateral,
elements of “mortgage redlining” can appear.
In its most extreme form, mortgage redlining
refers to neighborhoods in which mortgages
cannot be obtained through conventional chan­
nels. Recent data collected under the Home
Mortgage Disclosure Act (HMDA) show that
mortgage applications of blacks and Hispanics
are rejected more often than those of whites.1
Some of this pattern of lending may be due to
banks’ having less information about the value
of the houses that blacks and Hispanics intend
to buy. When banks have poor information
about house values, they will tend to reject
more mortgage applications.
Another example arises in commercial lend­
ing, where banks attempt to estimate the likeli­
hood that businesses will succeed. There is
some evidence that during economic down­
turns, banks have a harder time knowing which
borrowers are likely to be profitable. As a
consequence, banks tend to raise their lending
requirements more during an economic down­
turn, creating a “credit crunch” that may pro­

1 See Glenn B. Canner and Dolores S. Smith, “Home
Mortgage Disclosure Act: Expanded Data on Residential
Lending,” Federal Reserve Bulletin 11 (November 1991), pp.
859-81.




JANUARY/FEBRUARY 1993

long the recession. Credit crunches, such as the
one commercial real estate is struggling to
emerge from now, are times in which some
classes of borrowers have difficulty obtaining
credit at any price.
Both examples involve a fundamental inter­
action between slower economic activity and
the amount of information available to lenders.
Because the lender knows less about the loan
and whether it will succeed, it is riskier to the
lender, who must charge more interest or re­
quire more collateral to earn a return. Because
the lender charges more, some borrowers bor­
row less or drop out of the market, which can
result in a sustained slowdown in economic
activity. The reduction in economic activity
leads to less information, less information leads
to a further reduction in economic activity, and
a vicious circle can ensue.
This dynamic interaction between informa­
tion and economic behavior involves an exter­
nality, which is defined as occurring when the
actions of one person or firm influence the
opportunities and choices of another as a by­
product. In this case, the externality is that the
failure of one borrower to conclude a loan
makes loans more costly and harder to obtain
for later borrowers. In economic theory, exter­
nalities hold an important place in that when
they exist, the “invisible hand” of the market­
place does not necessarily result in optimal
interactions. For information externalities, this
implies that government intervention—of the
right kind—may help to improve credit market
outcomes. In particular, government interven­
tion to reduce mortgage redlining may im­
prove society’s welfare, even when the profit
motive and not racial discrimination is the
proximate cause of the redlining. And mon­
etary policy to reduce interest rates may be a
useful way to prime the credit pump during
recessions.
Scholars have explored these and other as­
pects of the importance of information to lend­
ers. Two other sources of information prob­
FEDERAL RESERVE BANK OF PHILADELPHIA

Information Externalities: Why Lending May Sometimes Need a Jump Start

lems in loan markets are asymmetric informa­
tion and coordination problems. These are
discussed in More Information Problems in Loan
Markets.
INFORMATION ABOUT COLLATERAL:
MORTGAGES, APPRAISALS, AND
REDLINING
Let’s start with the information problem that
can contribute to redlining in mortgage mar­
kets: assessing the value of the house that
serves as collateral for the loan.2 Lenders rely
on the sale prices of recently sold comparable
houses to help gauge the value of the house
being mortgaged. When there are few such
comparable houses, the value of a house is
harder to estimate, and lending on the collat­
eral of such houses is riskier. Bankers then may
become reluctant to lend in these areas.
The mortgage market operates in this way:
when a house is bought, the purchaser typically
obtains a mortgage loan to cover most of the
purchase price. This mortgage loan generally
requires the purchaser to make a down pay­
ment as well as pay “closing costs”—the vari­
ous fees, taxes, and escrow payments associ­
ated with the transaction. This down payment,
which ensures that the house is worth more
than the loan, plays a crucial role in the mort­
gage loan.
The lender has an important stake in the
down payment because when the house is
worth substantially more than the loan, the
lender is doubly protected against loss.3 First,
the homeowner fears losing the house and will
be unlikely to default. Second, if the homeowner

Leonard I. Nakamura

cannot make payments, the house is more likely
to be sold for more than the value of the loan, in
which case the lender will receive full repay­
ment of principal and accrued interest.
In a first mortgage that accompanies the sale
of a house, the sale price is a matter of public
record and indeed may shed some light on the
size of the down payment. Unfortunately, for
various reasons, the very real danger exists that
the sale price overstates the likely resale value
of the house.4
To safeguard against this, houses are typi­
cally appraised: a professional appraiser is asked
to estimate the market value of the house. In
the most common method of appraisal for
existing single-family houses, an appraiser finds
at least three recently sold houses that are
similar to the house in question and are in the
neighborhood. The appraiser, after adjusting
the prices of the three “comparables” by adding
or subtracting the value of features by which
they differ from the house being appraised,
weights the three adjusted values to come up
with an estimate of the market value of the
house being appraised, the appraised value.
The “mortgage value” of the house is then
calculated as the lesser of sale price or the
appraised value.
Now consider a neighborhood in which there
have been few recent sales. In this case, the
appraiser must use house sales that are out-ofdate or otherwise quite different from the house
being appraised. The estimate that the ap­
praiser then comes up with is likely to be less
reliable and require more judgment on the part
of the appraiser.
This will make it difficult for prospective
home buyers to obtain financing for two rea-

2This section is based on William W. Lang and Leonard
I. Nakamura, “A Model of Redlining,” Journal o f Urban
Economics (forthcoming).
3The use o f collateral in mortgages and lending gener­
ally is d iscu ssed in Leonard I. Nakamura, “Lessons on
Lending and Borrowing in Hard Times,” this Business Re­
view (July/August 1991), pp. 13-21.




4One danger is that the buyer may have simply overpaid
for the house. Another is that the buyer and seller may
inflate the sale price of the house to reduce the borrower’s
down payment. Nakamura (1991) discusses explicitly how
a seller’s offer to pay closing costs inflates the sale price.

5

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JANUARY/FEBRUARY 1993

More Information Problems in Loan Markets
The information externalities addressed in this article are not the only information problems
scholars have identified in credit markets. Two aspects of information that are also likely to be
important in credit markets are information asymmetries and coordination problems.
First, in any economic interaction the parties have different knowledge of relevant information: a
borrower will know more about her own business than the banker, and the banker may know more
about business conditions generally than the borrower. These differences in information are called
information asymmetries. Second, in many markets it is important to know the intentions of other
market participants: the problem of economic coordination.
Information Asymmetry in Loan Markets. One extreme example of information asymmetry is
loan fraud: borrowers know whether they are frauds or not, while lenders cannot always discern
fraudulent borrowers. When interest rates rise, the fraudulent borrower is unaffected because he or
she is never going to repay the loan. Some good borrowers, on the other hand, may well decide to wait
to borrow until rates fall again. The increase in rates worsens the average quality of borrowers—and
forces the lender either to raise rates even further or to increase the required collateral.
A similar mechanism operates whenever the lender has less information about the borrower’s
business prospects than the borrower does. A borrower who knows that the lender has underesti­
mated the borrower’s true risk and is charging too little interest is more likely to continue to borrow
after an interest rate increase than a borrower who knows the lender has overestimated the borrower’s
true risk and whose interest rate is too high. Thus an increase in interest rates will generally cause
unusually good borrowers to reduce their borrowing more than unusually bad ones.
Now, the information externality discussed in the main body of this article can lead to a decline in
the information available to lenders during recessions. If this reduction in lender information worsens
the information asymmetry between lenders and borrowers, making it harder for lenders to tell worse
borrowers from better borrowers, then the two problems will reinforce one another.
Coordination Problems in Loan Markets. A basic problem of economic coordination is selffulfilling prophecies. If one bank believes that other banks are unwilling to lend in a particular city
and that business prospects in that city will worsen as a result, the first bank will itself be unwilling
to lend to businesses in that city. Thus if all banks begin to think that other banks are unwilling to lend,
none of them may lend: the prophecy could be self-fulfilling. If, on the other hand, the banks
coordinate their lending, it might be possible for lending (and business in the city) to revive.
Some economists find self-fulfilling prophecies unlikely because formal models of this phenom­
enon require that the prophecy be fulfilled exactly, an unlikely occurrence in any actual economy. But
for actual economies, adverse expectations that are only partially self-fulfilling may persist for long
periods. Of course, a perspicacious investor may be able to profit from market mistakes of this kind,
but even so, the misperceptions may disappear only very slowly.3
Coordination problems among lenders may exacerbate redlining. If lenders desert a neighbor­
hood, default on a mortgage loan there is likely to be very costly to the lender, since selling the house
will become very difficult. One reason for the success of the Delaware Valley Mortgage Plan is that
it is a coordinated plan to which a number of the leading banks in the area have committed themselves.
aFor example, in the stock market, bad news about a stock apparently often drives the stock’s value below its true
worth. A result o f this, confirmed by research by Bruce Lehmann and others, is that “contrarian” stock purchase
strategies, buying stocks that have done poorly in the recent past, have consistently outperformed the U.S. market
average. Thus the U.S. stock market has apparently suffered from misperceptions that the profit motive has been very
slow to eradicate. Note that these adverse expectations about stocks increase the cost of raising funds for these
com panies, which would tend to make the adverse expectations self-fulfilling. See Bruce N. Lehmann, “Fads,
Martingales and Market Efficiency,” Quarterly Journal o f Economics, 105 (February 1990), pp. 1-29, where he examines
the performance o f portfolios of stocks chosen according to a contrarian rule. He shows that a portfolio of stocks
rebalanced w eekly to reflect the previous w eek ’s returns compared to the market (with losers more strongly
weighted) outperformed a balanced portfolio in all of the 98 quarters studied in the period from 1962-1986.

Digitized for
6 FRASER


FEDERAL RESERVE BANK OF PHILADELPHIA

Information Externalities: Why Lending May Sometimes Need a Jump Start

sons. First, the appraised value will more often
be inaccurate and will be too high or too low
more often than when better information on
comparable sales is available. This reduces the
value of the house whenever the appraised
value comes in too low but doesn’t raise it when
the appraised value comes in too high. On
average, appraised values will reduce mort­
gage values of houses, and larger down pay­
ments will be required. The need for a down
payment remains an important barrier to home
ownership for many households; larger down
payments raise the height of the barrier.5 Sec­
ond, the mortgage lender will typically be able
to see that the appraisal is inaccurate.6 Even
when the appraised value is above the sale
price, the lender may be unwilling to lend
because he or she implicitly discounts the ap­
praisal.
Of course, when buyers are few, unsuccess­
ful sellers must take stock and make a decision.
Some will decide to rent, rather than sell, which
reduces the public inform ation available
through house sales. Others will choose to
lower their prices, but then falling prices will
compound the riskiness that lenders perceive
in these markets.
Thus once a neighborhood suffers a slow­
down in house sales, difficulties in obtaining

5See Peter Linneman and Susan Wachter, “The Impacts
o f Borrowing Constraints on H om eownership,” AREUEA
Journal 17 (1989), pp. 389-402, for a discussion of the down
payment constraints in house purchases. One might think
that recent changes in m ortgage markets, including the
introduction o f a wide variety of types of mortgages, would
elim inate down payment constraints as a barrier to house
purchase. Linneman and Wachter’s evidence is that down
payments remain a significant barrier to house purchase:
prospective buyers with less cash available for a down
payment cannot buy as large a house as they otherwise
w o u ld .
6The mortgage lender will see that the sales used in the
appraisal comparisons are either far from the house being
appraised or are out o f date.




Leonard I. Nakamura

mortgages may perpetuate the difficulty in
finding buyers. Ignorance about house sale
values may thus feed on itself: the fewer the
sales, the less information lenders have and the
more likely they are to reject new loan applica­
tions, so even fewer sales occur. If lenders are
sufficiently chary of lending in such a neighbor­
hood, they might refuse to make any mortgages
there, the extreme form of mortgage redlining.
This response on the part of the lender, while
explainable in economic terms as an individual
business practice, is not socially optimal. In­
deed, since the passage of the Community
Reinvestment Act of 1977, a bank’s refusal to
lend in neighborhoods in its market area may
subject it to regulatory restrictions, reflecting
the belief that banks have an obligation to their
local communities to help them avoid lending
traps such as “redlining.” This type of govern­
ment intervention in the marketplace can be
justified theoretically, since the problem arises
because of an externality (see Externalities and
the Coase Theorem). Externalities imply that
market decisions may not be social optimums
because social benefits are not simply the sum
of the private benefits to the parties to the
market transaction. In this case, the externality
is that a current house sale reduces the cost and
increases the availability of mortgages to future
house buyers.
This line of reasoning helps us understand a
conundrum. This conundrum arises because
many researchers believe that redlining results
from discrimination by mortgage lenders. But
mortgage markets appear to be highly competi­
tive: entry into them is extremely easy, and
there are dozens, if not hundreds, of mortgage
lenders in the urban markets where redlining is
alleged to occur. If good credit risks are being
denied credit because of discrimination, why
don’t nondiscriminatory lenders enter these
mortgage markets and profitably end redlining?
Why is government prodding desirable? The
answer is that at least some redlining occurs
because inform ation is in short supply in
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JANUARY/FEBRUARY 1993

Externalities and the Coase Theorem
An externality exists when the actions of one or more economic agents affect the costs or benefits
of another as an unmarketed by-product. For example, if the air pollution that is a by-product of a
coal-burning electric generator lowers the demand for the services of a nearby hotel, an externality
exists. When externalities exist, market pricing may not provide the right incentives to maximize
social welfare. However, Nobel Prize winner Ronald Coase has pointed out that many externalities
can be cured by the action of the free market provided that transaction costs are not too large.
Let’s begin by briefly summarizing the argument that in the presence of externalities, competitive
markets may not lead to socially optimal outcomes. In competitive markets without externalities, the
invisible hand theorem assures that the marketplace provides appropriate social incentives for
productive activities. The price of a good simultaneously reflects the private cost of production of an
additional unit and the private benefit of consumption of an additional unit, which, when there are
no externalities, are equal to the social cost and benefit, respectively. With externalities, on the other
hand, part of the social cost or benefit of production is not reflected in the price of the good produced,
so that social costs and benefits diverge from price and, in general, private production is either too
little or too much.
One traditional example is bees and orchards. Beekeepers own hives and sell honey; orchard
growers own trees and sell fruit. These two activities are intertwined, as the bees use nectar from the
fruit trees to produce the honey, and fruit trees need the bees to pollinate their flowers. If the market
for honey is weak and beekeepers reduce the size of their operations, orchard growers may suffer
because their trees produce less.
Thus, prices in the honey market may not provide adequate social incentives to the beekeeper
because they do not take into account the benefits that orchard growers derive from the bees. Before
Coase’s analysis, the presumption was that government intervention was proper when such an
externality was known to exist. In the honey market example, a subsidy to beekeepers might be
desirable.
What Coase pointed out is that beekeepers and orchard growers can, and in many cases do,
contract privately between themselves to solve the problem created by the externalities. Each
producer can be thought of as producing a joint product: the beekeeper produces honey and
“pollination services”; the orchard grower produces fruit and “floral nectar.” If pollination services
are scarce, the orchard grower can pay a beekeeper to install hives in the orchard, providing the
beekeeper an income for “pollination services.” Or if there are too many bees and too few orchards,
beekeepers can pay orchard growers for “floral nectar rights” by renting space in orchards. The
fundamental idea is that if the relevant parties can be brought together with an assignment of property
rights, private contracting will result in providing the right incentives provided no transaction costs
exist.
Government intervention, according to Coase, should be sought only when the relevant parties
cannot be easily brought to the negotiating table or if bargaining costs are likely to be large, that is,
when transaction costs are high. To see where this may be necessary, fishing is a good example. The
catch of one fishing boat may reduce the catch of other boats, and the collective catch of the fishing
fleet in one year may reduce the catch in succeeding years.
Fishers may wish to write a contract that restricts their catch, but it may be hard to prevent entry
by others who haven’t signed the contract. An even more difficult problem is that future generations
of fishers (and fish eaters) may not be adequately represented in the absence of government
intervention on their behalf. As a consequence, government restrictions on fishing rights may
improve on private arrangements.
Coase’s idea directs us toward a clarification of exactly what transaction costs are and how they
impede a private market solution. In the case of the financial markets we are discussing, because
potential beneficiaries of improved information include future generations of borrowers and lenders,
private market incentives are likely to be inadequate.
8 FRASER
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FEDERAL RESERVE BANK OF PHILADELPHIA

Information Externalities: Why Lending May Sometimes Need a Jump Start

redlined neighborhoods. And as long as the
information remains in short supply, profitable
entry is not possible.
The dynamic information externality does
not explain how redlining gets started, but
rather why it is self-perpetuating. Redlining
may begin from a discriminatory practice or
from a temporary neighborhood decline. For
example, the Depression of the 1930s had a very
deep impact on the Harlem neighborhood in
New York. Although not the only factor, infor­
mation externalities help explain why the De­
pression might have hurt mortgage lending in
Harlem long after the U.S. economy as a whole
had returned to normal.
Also, the size of down payments is part of the
reason redlining is self-perpetuating. In wealthy
neighborhoods, where potential home buyers,
on average, can afford larger down payments
when they are required, uncertainty about house
values will not retard sales nearly as much as in
poorer neighborhoods, where down payments
are critical barriers to homeownership.
REMEDIES FOR REDLINING
If a key problem in “redlining” is an informa­
tion externality that increases the costs and
risks of lending, appropriate remedies must
take this into account. In particular, in neigh­
borhoods where appraisals are less reliable,
mortgage makers may need to be prodded to
gather additional information about the house
or borrower in question. Local community
groups may be helpful in providing more de­
tailed information about specific blocks and
changing neighborhood boundaries. And with
house equity a less secure source of repayment,
the character of the borrower may become
more important. Here again, local community
groups may be useful in screening applicants.
The Delaware Valley Mortgage Plan (for­
merly called the Philadelphia Mortgage Plan) is
one of the more successful banking coalitions
aimed at attacking redlining.7 Three key fea­
tures of the mortgage plan result in relatively



Leonard I. Nakamura

strong lending across diverse neighborhoods.
First, all banks commit themselves to acquiring
more information by lending on the basis of the
specific block the house is on and by looking
thoroughly for m itigating factors when a
borrower’s credit records are not spotless. This
is particularly important in maintaining the
stability of neighborhoods where some blocks
have deteriorated but others have been main­
tained or upgraded. Second, the plan reduces
the effective cost of transactions to the appli­
cant. All applications recommended for rejec­
tion under the plan are reviewed by a credit
committee to ensure that credit decisions are
free of bias and consistent with the plan’s poli­
cies. The committee can recommend that the
bank reconsider its decision, and if the bank
persists in declining the application, another
member bank can consider the application.
Thus each application is, in effect, an applica­
tion to all the member banks, which directly
reduces the applicant’s transaction costs. Fi­
nally, the plan relies on extensive outreach, and
referrals from community organizations play
an important role in increasing applications
under the plan.
These elements of the Delaware Valley Mort­
gage Plan together form a sensible and unusu­
ally successful attack on the underlying infor­
mation problems that play an important role in
redlining. Of course, the additional informa­
tion and committee work are not costless. As a
consequence, competitive pressures can erode
lenders’ willingness to participate in plans such
as these. So legislation that requires mortgage
lenders to take positive steps to support com­
munity borrowing can have a valuable role in
making these types of plans viable.
Another approach is to reduce the down

7For a full discussion of the Delaware Valley Mortgage
Plan, see Paul S. Calem, “The Delaware Valley Mortgage
Plan: An Analysis Using HMDA Data,” Journal o f Housing
Research (forthcoming).

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BUSINESS REVIEW

JANUARY/FEBRUARY 1993

payment constraint. The federal government
assists mortgage borrowers with two mortgage
loan programs, one run by the Federal Housing
Administration (FHA) and the other by the
Veterans Administration (VA). Both programs
relax the down payment constraint and thus
tend to make sales possible in neighborhoods
that are informationally constrained.8 The FHA
mortgage program has the drawback that it
requires a 3.8 percent mortgage insurance pre­
mium, making them more expensive than con­
ventional mortgages with private insurance.
VA mortgages are subsidized and less expen­
sive than conventional mortgages but are avail­
able only to veterans. One concern that remains
with these programs, as was emphasized in the
discussion of the importance of the down pay­
ment to the lender, is that lower down pay­
ments tend to result in greater loan losses. In
fact, delinquency and foreclosure rates are
higher on FHA and VA mortgages. Thus while
reducing down payments helps to make sales
possible, it simultaneously increases the risk of
undesirable outcomes. Indeed, it is conceiv­
able that increased foreclosures in these gov­
ernment programs may, in certain circum ­
stances, add to perceived risk in conventional
mortgage lending. Thus reducing the down
payment constraint cannot be viewed as a com­
plete solution to the information problems in
mortgage markets.
Thus far we have discussed the value of
information in mortgage lending. For commer­
cial loans, information—about the purpose of
the loan and about the likelihood of the busi­
ness success of the borrower—is often crucial

to sound lending. Information about the de­
mand for a proposed product or service, for
example, is a key input to commercial lending:
the success of a pizza parlor in a town is useful
in judging the likely success of a pizza delivery
service. It is to this type of information that we
now turn.

8See, for example, Stuart S. Rosenthal, John V. Duca, and
Stuart A. Gabriel, “Credit Rationing and the Demand for
Owner-Occupied Housing,” Journal o f Urban Economics 30
(July 1991), pp. 48-63, for evidence that holders of VA and
FHA mortgages face reduced noncredit constraints such as
down paym ents.

9This section is based on William W. Lang and Leonard
I. Nakamura, “Information Losses in a Dynamic Model of
Credit,” Journal o f Finance 44 (July 1989), pp. 731-46, and
“The Dynamics of Credit Markets in a Model with Learn­
ing,” Journal o f Monetary Economics 26 (October 1990), pp.
305-18.

10 FRASER
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INFORMATION ABOUT BUSINESS
OPPORTUNITIES: COMMERCIAL
LENDING AND CREDIT CRUNCHES
In this section, we focus on the special role of
commercial banks in the financing of small
businesses. The essence of this role is that banks
must make judgments about whether busi­
nesses that ask for loans are likely to succeed. In
making these judgments, each bank relies on
information generated by past lending activity,
its own and that of other banks. Hence, an
information externality may be a source of
“credit crunches” during recessions.
In the parable of perfect competition taught
in undergraduate microeconomics, entrepre­
neurs are constantly searching for profit oppor­
tunities. If an entrepreneur is successful and
achieves supranormal profits, other entrepre­
neurs observe this success, copy it, and elimi­
nate the short-run profits of the pioneer. We
thus are called to witness the triumph of the
“invisible hand,” and we are told this is a social
optimum. In a static sense, it is. But this
narrative describes a dynamic information ex­
ternality: the information generated in one pe­
riod is valuable for the allocation of resources in
succeeding periods.9
Bank lenders are a crucial part of this infor-

FEDERAL RESERVE BANK OF PHILADELPHIA

Information Externalities: Why Lending May Sometimes Need a Jump Start

relational chain in two ways.10 First, the past
loans that commercial bank lenders have made
are unique sources of detailed data about the
local economy and individual enterprises. In
the course of making loans, banks typically
obtain information about the borrower not else­
where available. In particular, because of their
access to the checking accounts of their borrow­
ers, banks acquire more detailed information
than other possible lenders.11
Second, to accurately evaluate the default
risk of commercial borrowers, bank lenders
continually compile and analyze both local and
national information. For example, Robert
Morris Associates, the association of bank loan
and credit officers, collects and disseminates
summary information from financial statements
of different borrowers, classified by industry.12
This enables lenders to compare the financial
statements of borrowers with national industry
norms. Of course, because local conditions are
crucial to local lending, bank loan officers must
be experts on their local economies and must
continually search out information about local
conditions.
Information about borrowers is of value in

10A classic discussion of the informational role of banks
is found in Douglas B. Diamond, “Financial Intermediation
and Delegated Monitoring,” Review o f Economic Studies 5 1
(July 1984), pp. 393-414.
"Fischer Black, “Bank Funds Management in an Effi­
cient Market,” Journal o f Financial Economics 2 (1975) and
Leonard I. Nakam ura, “Com m ercial Bank Information:
Implications for the Structure of Banking,” in Lawrence J.
W hite and M ichael Klausner, eds., Structural Change in
Banking , Irwin (forthcoming); both discuss aspects of the use
o f checking accounts as sources of information for commer­
cial banks.

12Annual Statement Studies , Robert Morris Associates,
Philadelphia.

Robert Morris Associates also publishes the
Journal o f Commercial Lending , which regularly includes ar­
ticles on lending to particular industries.




Leonard I. Nakamura

lending to large and small borrowers alike.
However, banks have a relative advantage in
lending to small, relatively risky borrowers
rather than large, relatively safe borrowers
because the local information banks are able to
gather about their borrowers is of most value in
lending to smaller, riskier borrowers.13 Recent
evidence on loans to smaller borrowers studied
by Timothy Hannan shows that such borrow­
ers pay higher interest rates in concentrated
banking markets, that is, markets in which
there is less competition among local bank
lenders.14 This implies that nonbank lenders (or
nonlocal banks) find it harder to enter these
markets to provide a check on the market
power of the local banks, presumably because
the nonbank lenders lack the local information
that the local banks have.
Because past and existing loans convey in­
formation that is useful in the making of new
loans, a decline in local lending and the con­
comitant decline in economic activity will tend
to make future lending riskier. Bank lenders
will have less information upon which to judge
new applications, and that will make them
more uncertain in their judgments.
This leads banks to raise their risk premiums
in lending, which in turn makes borrowing
riskier for the borrowers, who face a higher

13See Nakamura, “Com m ercial Bank Inform ation...”
(forthcoming) for a fuller discussion of the evidence for this
proposition. Specific evidence on bank lending to hospitals
is in Paul S. Calem and John A. Rizzo, “Banks as Information
Specialists: The Case of Hospital Lending,” Journal o f Bank­
ing and Finance (forthcoming). Theory and statistical evi­
dence suggest that sm allness and riskiness are associated
and that both contribute to a firm ’s dependence on bank
lending. It should be pointed out, however, that there exist
large, risky firms and small, safe ones.
14Timothy H. Hannan, “Bank Commercial Loan Markets
and the Role of Market Structure: Evidence from Surveys of
Commercial Lending,” Journal o f Banking and Finance 15
(February 1991), pp. 133-49.

11

BUSINESS REVIEW

repayment15 and a greater risk of bankruptcy.
Note that the borrower’s business prospects
need not have changed. But although the
expected return to the borrower’s business is
the same, the lender perceives a greater risk
because lenders have less information. The net
effect is a higher probability of bankruptcy for
the borrower; the lender’s uncertainty becomes
greater risk for the borrower.16 Borrowers then
borrow less; the higher risk causes them to
lower their planned economic activity. The
higher risk faced by borrowers reduces their
borrowing both because of the borrower’s aver­
sion to risk and because of the increased costs
businesses face when financial distress occurs.
Banks also attempt to counter the loss of
information by making the noncredit terms of
lending more onerous—requiring more collat­
eral or personal guarantees. The higher non­
credit terms may be impossible for the bor­
rower to meet, or the borrower may feel the
terms entail an unacceptable degree of personal
risk.
The loss of information may be exacerbated
if the bank decides that specific types of lending
are unlikely to be profitable for a sustained

15The banks require a higher risk premium because as
risk increases, the expected return to the loan decreases.
With higher risk, the borrower fails to repay the full amount
o f the loan more often. Of course, if the bank is risk averse
and cannot fully diversify the risk o f the loan, the risk
premium will increase by even more, and the impact of the
information externality w ill be even greater.
l6This analysis applies with both risk-averse and riskneutral lenders. In the risk-neutral case, the greater uncer­
tainty on the part o f lenders implies that lenders will more
often be either too optimistic or too pessimistic than when
there is more information available. The borrower is then
sometimes charged too much and other times too little. The
uncertainty o f the lender randomly redistributes borrowing
costs across borrowers, which increases the effective risk
faced by the average borrower. This increase in borrower
riskiness results in a higher probability o f bankruptcy and
thus greater average borrowing costs for borrowers.

Digitized for
12FRASER


JANUARY/FEBRUARY 1993

period of time or that the bank is carrying too
much risk exposure in one area already. The
bank may transfer personnel away from that
area or lay them off, thus further reducing the
information the bank has.
These information problems in lending are
local. Are they important for entire economies?
This is primarily an empirical matter. But on a
theoretical level, economywide shocks can
clearly be prolonged by this essentially local
mechanism. If, for example, an oil price hike
leads to fewer loans being made across the
country, each local credit market thereafter has
less information, which will in turn tend to
reduce local lending in each market in the next
period, creating an economywide impact of
reduced lending.17* As a result, the aggregate
temporary dislocation can have prolonged ef­
fects through local channels. Similarly, the
economywide impact of reduced mortgage
lending can exacerbate and prolong recessions
in the housing market.
Thus a temporary decline in aggregate lend­
ing may become prolonged because, in addi­
tion to the normal dampening effects of a re­
duction in demand on economic activity, banks
also face a reduction in the information avail­
able to them about borrowers. As a conse­
quence, banks will tend to reduce their hold­
ings of loans to risky borrowers and increase
their holdings of loans to less risky borrowers
(including, possibly, their holdings of U.S. gov­
ernment and agency debt).
This means that recessions will, from the
borrower’s perspective, typically be character­
ized by periods of relatively tight credit, when
little new lending is going on. Also, banks have
less information during these periods, making
it somewhat harder for them to discern when
times are improving and new loans less risky.
The problem of information loss during re-

l7See Lang and Nakamura (1990) for the underlying
theory.

FEDERAL RESERVE BANK OF PHILADELPHIA

Information Externalities: Why Lending May Sometimes Need a Jump Start

cessions and the fact that this information loss
constitutes an externality imply that too little
lending occurs during recessions. As a conse­
quence, the monetary authority may wish to
encourage lending by pushing short-term in­
terest rates down to offset this effect during
recessions.
In the absence of the externality, interest
rates should reflect and mediate, in Irving
Fisher’s classic terminology, the impatience to
spend (the preference for present over future
consumption) and the opportunity to invest
(the return on capital investment).18 With no
information externality, actions of the mon­
etary authority to raise or lower the interest rate
interfere with this equilibrium.
The transmission of information through
credit markets is likely to be least efficient
during recessions, as the evidence below points
out. With this information externality, then, a
reduction in interest rates during a recession
may indeed improve social welfare by encour­
aging additional lending, which provides in­
formational advantages as the recession turns
to recovery.
Empirical Evidence on Information Losses
During Recessions. The information theory
just outlined applies primarily to smaller, riskier
firms that are more dependent on bank credit.
Larger, safer firms have access to nonbank
sources of funds. A decrease in bank lending to
these risky firms could have a prolonged effect
on the availability of credit to these firms and
thus on their economic activity if the theory is
important empirically.19-20
Data taken from the Federal Reserve’s Sur-

l8Irving Fisher, The Theory o f Interest. N ew York:
Macmillan, 1930. This classic work is subtitled, “As Deter­
mined by IMPATIENCE To Spend Income and OPPORTU­
NITY To Invest It.”
l9If the junk bond market, for example, could substitute
fully for banks in lending to risky borrowers during reces-




Leonard I. Nakamura

vey of Terms of Bank Lending suggest that
recessions indeed appear to be foreshadowed
by a “flight to quality” in which the ratio of
“safe” commercial loans (to borrowers consid­
ered “prime” customers) to total commercial
loans (the sum of prime and less than “prime”
borrowers) increases.21 Although these data
are available only since 1979, all three reces­
sions since then were foreshadowed by a flight
to quality, as measured by the ratio of safe
lending to total lending.
In each case, the flight to quality signals a
persistent shift in real U.S. economic activity, as
the theory just outlined suggests. The impact of
a reduction in risky lending on real U.S. growth
(as measured by real gross domestic product)
grows for at least a year and persists strongly
for at least two years.
Balance sheet data on corporate liabilities
also support the point that small borrowers are
affected crucially in recessions. Stephen Oliner
and Glenn R udebusch show that a decline in

bank lending following a monetary contraction

sions, decreases in bank lending to these borrowers would
likely not have much aggregate importance. The empirical
evidence that fo llo w s show s that recent changes in the
market structure o f lending have not eliminated the aggre­
gate importance of bank lending.
20The empirical question of whether credit disturbances
play an important role in aggregate activity has been a
recurrent one in economics. Ben Bernanke, in an influential
article, has given evidence that the Depression of the 1930s
was exacerbated by the bank failures that were endemic at
that time. He argued that the bank failures led to a greater
cost o f financial intermediation: investment became more
difficult because the bank failures greatly compromised the
banking system’s ability to evaluate and monitor loans. See
Ben Bernanke, “Non-Monetary Effects of the Financial Col­
lapse in the Propagation of the Great Depression,” American
Economic Review 73 (June 1983), pp. 257-76.
2lThe empirical evidence discussed here is in William
W. Lang and Leonard I. Nakamura, “The Flight to Quality in
Bank Lending,” Working Paper 92-20, Federal Reserve Bank
of Philadelphia, 1992.

13

BUSINESS REVIEW

JANUARY/FEBRUARY 1993

depresses investment spending by small firms.22
In a separate test, they show that small firms are
more dependent on internal cash flows for
investment during a recession.
Data on sales by small firms also tend to
support this argument. Mark Gertler and Simon
Gilchrist show that after a monetary tightening,
the sales growth of small firms declines more
sharply than that of large firms.23
These papers strongly suggest that reduc­
tions of bank lending to smaller, riskier bor­
rowers are an important element in recessionary
periods. They argue that bank loans to small
firms are important to aggregate activity, mak­
ing it appear likely that information externali­

22Stephen D. O liner and Glenn D. Rudebusch, “The
Transmission o f Monetary Policy to Small and Large Firms,”
mimeo, Federal Reserve Board, June 1992.
23Mark Gertler and Simon Gilchrist, “Monetary Policy,
Business Cycles and the Behavior of Small Manufacturing
Firms,” NBER Working Paper No. 3892, 1991.

ties exacerbate declines in economic activity.
SUMMARY AND CONCLUSIONS
Commercial banks are information special­
ists. By and large, competition and the profit
motive provide good incentives for banks to do
an excellent job of information-gathering and
loan analysis. Lenders depend, however, on
past transactions to provide information to
help them evaluate current loans. Periods in
which loan markets become thin thus tend to
become self-perpetuating, as the slowdown in
lending reduces information and the resulting
ignorance begets uncertainty and makes bor­
rowing riskier, so even fewer loans are made.
This represents a dynamic information exter­
nality. As a consequence, when information
thins out, as when few mortgages are made in
a given neighborhood or when lending to risky
borrowers declines in a recession, there may be
a useful role for the government to play in
encouraging credit activity.

Other Dynamic Information Externalities
Information externalities are crucial to credit markets because the provision of credit is so
intimately tied to information. Yet, information externalities exist not only in credit markets but
throughout the economy.
For instance, Rafael Rob has shown that information externalities are important to capacity
decisions in growing industries.1 Rob’s analysis begins with the point that in such industries,
producers will be uncertain about the shape of the demand curve and must guess about how much
capacity the market will bear. Each addition to capacity—as it comes to market—provides additional
information about the demand for the product. This information is then of value to the next firm that
adds capacity.
Another area in which information externalities are important is in new inventions and ideas.
Although patent protection allows an inventor to keep some of the value of a new idea or invention,
subsequent inventions and ideas that build upon it can appropriate much of this value. Basic research
is subsidized for this reason, since basic research may have little immediate market value but may
have great ultimate social value, value garnered by those who build on the original idea.
Indeed, the development of any new industry is likely to be rife with instances of firms’ benefiting
from the risks and ideas of others.
For example, many personal computer manufacturers have
benefited from the firms, such as Apple and IBM, that pioneered this market. If this is the case, society
will benefit when research and development are subsidized.
aRafael Rob, “Learning and Capacity Expansion Under Demand Uncertainty,” Review o f Economic Studies 58 (July
1991), pp. 655-75.

Digitized 14
for FRASER


FEDERAL RESERVE BANK OF PHILADELPHIA

Predicting Stock-M arket Volatility
D. Keith Sill*
O n October 19, 1987, the stock market posted
its largest one-day decline ever when the Dow
Jones Industrial Average fell 508 points, a drop
of over 22 percent in a single day. Prior to the
crash of 1987, the largest single-day drop in the
stock market occurred on October 29, 1929,
when the market fell by about 13 percent. While
drops of this magnitude are rare, it is not
uncommon for stock prices to rise or fall by 3
percent or more in a single month. Stock prices
seem to be very unpredictable. In addition,

* Keith Sill is an Economist in the Research Department
o f the Philadelphia Fed.




economists have long recognized that stock
prices go through turbulent and tranquil peri­
ods. Turbulent periods are times of high uncer­
tainty when stock prices move sharply from
month to month; tranquil periods are times
when stock price movements are much more
subdued.1 However, only recently have econo­
mists begun modeling how stock-market vola­
tility (or stock-price turbulence) changes
through time.
'This recognition o f the changing variability o f stock
prices goes back to the early 1960s. An early, comprehen­
sive study of the behavior of stock-market prices is that of
Fama (1965).

15

BUSINESS REVIEW

Why does stock-m arket volatility vary
through time? Is stock-market volatility pre­
dictable? To address these questions we will
need to examine theories about how stock prices
are determined. Then we can see whether the
behavior of U.S. stock prices over the last 30
years is consistent with the implications of
these theories. But first, we would like to know
how stock-market volatility affects the economy.
HOW DOES STOCK-MARKET
VOLATILITY AFFECT THE ECONOMY?
Economists argue that stock-market volatil­
ity can affect the economy in several ways: (1)
it influences how much people spend and save;
(2) it influences the prices of stocks; and (3) it
influences the prices of financial options and
thus affects how investors might hedge invest­
ment risk.
The Effect on Spending and Saving. How
might an increase in stock-market volatility
affect people’s spending and saving decisions?2
Consider the case of a hypothetical person
named Walter Wealthy who has an uncertain
future income because of his investments in the
stock market.3
Walter’s decision about how much to spend

2In the following discussion of the effects of stock-return
uncertainty on people’s spending and saving decisions, we
get the sharpest predictions by assuming that stocks are the
only risky assets in which people can invest. Alternatively,
we can assume that there are other risky assets but that an
increase in stock-return uncertainty reflects an increase in
return uncertainty of all risky assets. If the increase in stockreturn uncertainty is specific to the stock market, then the
primary consequence o f the increase may be a portfolio shift
away from stocks and into other assets. The overall effect on
spending and saving is then more difficult to pin down. For
details see the 1989 article by Robert Barsky listed in the
References.
3In general, part o f the income uncertainty that people
face is due to their future labor income being uncertain. In
the case o f Walter Wealthy we will ignore labor income
uncertainty in order to focus on the uncertainty associated
with holding risky assets such as stocks.

Digitized for
16 FRASER


JANUARY/FEBRUARY 1993

today depends on how much income he ex­
pects his stocks to produce. If he expects a high
return from his investment in stocks, he may
want to spend less (and save more) today.4
Doing this allows Walter to spend more in the
future (if the high expected return comes about).
This incentive to save more today is called the
substitution effect, since future spending is
substituted for current spending.

Offsetting this substitution effect is an in­
come effect, which leads Walter to want to
spend more today. If the expected stock return
is high, he feels richer today because he expects
to have higher wealth in the future. Feeling
richer, Walter may increase current spending.
Thus, the income effect works to offset the
substitution effect. However, empirical evi­
dence suggests that usually the substitution
effect dominates the income effect, so that sav­
ing increases with an increase in expected re­
turns.5
We have seen that the expected return on
stocks affects W alter’s spending and saving
decisions. His decisions also depend on the
degree of uncertainty about the return on stocks.
An increase in the degree of uncertainty means
that a stock’s expected return is unchanged, but
there is an increased chance that the actual
return will be farther away from the expected
return. For example, suppose you buy a stock
today for $100 that pays off $105 with a 10
percent chance, pays $110 with an 80 percent
chance, and pays $115 with a 10 percent chance.
The expected payoff on this asset is then (.10 x
$105)+(.80 x $110)+(.10 x $115) = $110. An
increase in uncertainty can come about either
by an increase in the likelihood of getting a high

^ h e return from holding stocks includes both the divi­
dends paid to the stockholder plus capital gains that accrue
when the price of the stock increases.
5For a fuller discussion of the income and substitution
effects associated with changes in uncertainty, see the ar­
ticles by Barsky (1989) and Abel (1988).

FEDERAL RESERVE BANK OF PHILADELPHIA

Predicting Stock-Market Volatility

or low payoff, for example, a 20 percent chance
of $105, a 60 percent chance of $110, and a 20
percent chance of $115 (note that the expected
return remains $110); or by a change in the
value of the high and low payoffs, for example,
a 10 percent chance of $100, an 80 percent
chance of $110, and a 10 percent chance of $120.
Again, the expected return is $110.
In the case of an increase in uncertainty, as in
the case of an increase in the expected return,
there are offsetting effects. A precautionarysaving effect induces Walter to cut back on
current spending and increase current saving.6
He increases current saving to guard against
the increased likelihood of a bad outcome,
which is a low return. On the other hand, a
substitution effect leads Walter to spend more
today. He spends more today in an effort to
sidestep the increase in risk because current
spending looks more attractive in the face of
increased uncertainty about the future.
Which effect dominates depends on Walter’s
attitude toward risk. If he has a strong-enough
dislike for risk, the precautionary-saving effect
dominates, so his current spending will fall,
and his saving will rise in response to an in­
crease in the uncertainty of returns.7 Empirical
studies of household preferences toward risk
suggest that most people fall into this category.
We can also consider how an increase in
uncertainty affects the current prices of stocks.
If Walter dislikes risk, an increase in the uncer­
tainty of returns on stocks can lead him to sell
some of his stocks and buy other, less risky
assets, such as bonds. Since other holders of
stock will also behave like Walter, the current
prices of the stocks will fall as people sell their
shares. Therefore, an increase in the uncer­

6For more on precautionary savings, see Barsky (1989)
and Blanchard and Fisher (1987).
7This increased savings will flow partly into assets that
are less risky than stocks.




D. Keith Sill

tainty of returns can lead to a fall in the current
price of stocks.
So, if Walter has a strong-enough dislike for
risk, an increase in uncertainty about stock
returns may cause him to increase current sav­
ing to guard against the possibility of a very low
return next period. Thus, increased stock-mar­
ket volatility can affect how much people spend
and save. In addition, increased uncertainty
can lead to a fall in the current prices of stocks.
The Effect on Stock Options Prices. An
increase in stock-market volatility also affects
another variable of economic interest: the price
of stock options. A stock option is merely a
contract that gives its owner the right to buy or
sell a specified number of shares of an underly­
ing stock at a specified price, called the exercise
(or strike) price, within a specified period. For
example, on July 3, 1992, as reported in the Wall
Street Journal, one could have purchased a call
option on Intel stock that would give the owner
the right to buy 100 shares of Intel at a price of
$55 per share on or before the third Friday in
August 1992. The price to purchase the con­
tract was $350, and Intel stock was selling on
the National Association of Securities Dealers
Automated Quotation (NASDAQ) system for
$55-7/8 per share.
Stock options are like insurance contracts:
the owner of a stock option has paid a “pre­
mium” to acquire “insurance” that eliminates
some of the downside risk associated with
holding a share of stock (the chance that the
price of the stock will fall dramatically). The
writer of the option contract acts like an under­
writer, agreeing to “insure” the buyer of the
contract against a bad outcome. Options are
used by investors, consumers, and producers
to hedge against uncertainty.
Investors and producers who use options as
part of their financial strategy are of course
interested in whether particular options are
priced appropriately. In a 1973 article, Fisher
Black and Myron Scholes developed a popular
and widely used model of option pricing that
17

BUSINESS REVIEW

JANUARY/FEBRUARY 1993

Because the downside risk on a call option is
shows how the price of an option can be deter­
mined from certain characteristics of the un­ limited and the potential gains on the upside
derlying stock. One of these characteristics is are not, the price of an option should be higher
the volatility of the stock price. In the Black- when the volatility of the stock price is high.
Scholes model, the higher the volatility of the The higher the volatility, the greater the chance
stock price, the higher is the price of the option.8 that at the option’s expiration date the underly­
The intuition behind this result can be under­ ing stock price will exceed the option’s exercise
stood without going into the complexities of price.
There is also an indirect path by which a
the model. With higher volatility of stock
prices, there is a greater chance of receiving change in uncertainty might affect the price of
both a good outcome (high stock price) and a a stock option. Recall that an increase in uncer­
bad outcome (low stock price). However, the tainty can lead to a fall in the price of a share of
option bears no downside risk. The worst that stock. A fall in the share price will in turn lead
can happen is that the option will expire worth­ to a decrease in the price of a call option written
less at maturity. Referring to our Intel example, on that stock. Suppose that a stock is trading at
suppose that the share price of Intel stock fell to a price that is below the exercise price of the call
$52 in August. Then the call option would option on that stock. If the share price falls, the
expire worthless, since no one would want to option would be less valuable, since the stock
exercise the option and purchase the stock for price will have to increase by a larger amount in
$55 when it could be bought on the stock order that, at the expiration date, the selling
market for $52. In that case, the option buyer price of the stock exceeds the exercise price of
would lose the $350 spent to purchase the the option. Thus, a fall in the current price of a
option. However, even if Intel fell to $1 per share leads to a fall in the price of a call option
share, the most that the option owner could lose written on that stock.
would be $350, the price of the option contract.
We see then that there are offsetting effects
Note that the owner of 100 Intel shares would on options prices due to a change in the uncer­
lose over $5400 dollars if the share price fell to tainty of a stock. For a call option, the direct
$1. On the other hand, if Intel’s price rises to effect of an increase in volatility is to raise the
$155 in August, the option owner would exer­ price of the option. The indirect effect is to
cise the contract and buy 100 shares for $55 per lower the price of the option through a change
share. She could then sell those shares for $155 in the current price of the share. For a put
per share and receive a profit of ($155 - $55)x(100 option, which gives the owner the right to sell
shares of the underlying stock at a fixed price,
shares) = $10,000.
direct and indirect effects of an increase in
volatility work in the same direction.
8We should note that in the Black-Scholes derivation of
We have seen two examples of how stockoption prices, it is assumed that the volatility of the stock
market volatility affects behavior. Increased
price is constant. Thus, when we compare the effects of
stock-market volatility causes people to spend
higher variance on option prices we are really comparing
less and save more, and for a given spread
options written on two different stocks. The arbitrage
argument used in the valuation procedure is not sufficient
between a stock price and option strike price, it
to determine the price o f the option when the option de­
raises the price of the option.
pends on variables that are not traded or that cannot be
hedged by an existing security, as is the case with stock price
volatility. When stock prices have a time-varying variance,
more restrictive equilibrium asset-pricing m odels can be
used to derive option prices.


18


HOW DOES STOCK-MARKET
VOLATILITY CHANGE OVER TIME?
We have seen how changes in stock-market
FEDERAL RESERVE BANK OF PHILADELPHIA

Predicting Stock-Market Volatility

D. Keith Sill

volatility can affect the economy. How has this sure the volatility of an asset is to look at its
volatility changed over time? To answer this variance. Variance is a measure of
question, we must first construct a measure of dispersion—the larger the variance, the more
spread out a distribution is. Another useful
the volatility of the stock market.
A graph (Figure 1) called a histogram illus­ concept for measuring volatility is the standard
trates the idea behind volatility. Panel A shows
annual returns on common stocks as measured
by the Standard & Poor’s 500 index (S&P 500),
9Note, however, that an investor is rewarded for taking
and Panel B shows annual returns on long-term
on the extra risk associated with holding common stocks.
government bonds. The height of the bars in The average return on common stocks over this period is
each panel represents the number of times about 11 percent per year. The average return on long-term
(frequency) a particular return was observed government bonds is 6.6 percent per year.
on a yearly basis from 1959
to 1991. A tall bar means
FIGURE 1
that a particular return was
Asset Return Distribution
observed relatively more of­
ten. The horizontal axis
(1959 - 1990)
measures annual return in
Distribution of Annual Returns on Common Stocks
percent.
In Figure 1, the three tall­
est bars in the bond-return
distribution account for
more than 65 percent of the
observations. In the com­
mon stock-return distribu­
tion, the three tallest bars
account for only slightly
more than 45 percent of the
observations. The distribu­
-80
-60
-40
-20
0
20
40
60
80
Annual Return (Percent)
tion of returns for common
stocks is more spread out
Distribution of Annual Returns
than is the return distribu­
tion for long-term bonds,
which means that there is a
higher likelihood of receiv­
ing either a high or a low
return when investing in
stocks versus investing in
long-term bonds. This sug­
gests that common stocks
are riskier investments than
government bonds, that is,
-80
-60
-40
-20
0
20
40
60
80
Annual Return (Percent)
stock returns are more vola­
tile.9
Source: Ibbotson Associates and author's calculations
One useful way to mea­



19

JANUARY/FEBRUARY 1993

BUSINESS REVIEW

deviation, which is defined as the square root of
the variance (see Calculating Variances and Stan­
dard Deviations for technical details on vari­
ances and standard deviations).10 In Figure 1
we saw that common stocks are more volatile
than long-term government bonds. This is
reflected in the statistic for the standard devia­
tion: annual stock returns have a standard
deviation of 15.6 percent, which is larger than
the standard deviation of annual government
bond returns of 10.8 percent.
Forecasting Stock-Market Volatility. People
need to forecast how volatile the stock market
is so that they can make better decisions about
spending and saving and about pricing op­
tions. You might think that the best forecast of
the volatility of the stock market is simply to
calculate the variance of stock returns from a
distribution like that shown in Figure 1. That
calculation shows that the long-run standard
deviation of annual stock returns is 15.6 per­
cent. But this is not the best forecast of the
variance at any particular date. Forecasts that
use recent information are more efficient than
forecasts that do not use recent information. If
stock-market volatility is high this month, that
may indicate an increased chance that volatility
will be high next month. If this is the case, we
want to use this information in making fore­
casts of stock-market volatility.
One method of forecasting the variance of
the stock market is to use time-series models.11 A

l0A helpful rule o f thumb is that 67 percent o f the
observations tend to fall within one standard deviation of
the mean, and 95 percent of the observations tend to fall
within two standard deviations of the mean. This rule of
thumb is for symmetric distributions, which means that the
tails o f the distribution are mirror images of each other.
"Alternative methods of deriving and forecasting stockreturn volatility are used as well. An estimate of the return
variance can be derived using option-pricing theory. In the
Black-Scholes model o f option pricing, the variables that
determine the current price o f the option are the current
stock price, the time to maturity o f the option, the strike




Calculating Variances
and Standard Deviations
Variance is a quantitative measure of how
spread out a distribution of variables is. The
variance is defined as the average value of
squared deviations of a variable from its mean.
If we have a sample of n observations on a
variable x, the general formula for variance is
given by:
n

a2=-^X(Xi-x)2
i=l

where

is the sample mean:
n

We can clarify this formula with a simple
example. Suppose a stock yielded 3 percent
one month, -2 percent the next month, and 1
percent and 6 percent in the following months.
The average return on the stock is, in units of
percent:
3 + (-2) + 1 + 6
4
~2
The variance is given by:
(3-2)2 + (-2-2)2 + (1-2)2 + (6-2)2 _ Q<

The standard deviation of returns is given by
the square root of the variance, or 2.92 percent.
The standard deviations reported in Figure 1
were calculated this way, using 32 observa­
tions on annual returns.
The standard deviation of stock returns
exceeds the standard deviation of bond re­
turns in Figure 1 because actual individual
stock returns are often quite different from the
average value of stock returns. Individual
government bond returns are usually much
closer to their average value.
FEDERAL RESERVE BANK OF PHILADELPHIA

Predicting Stock-Market Volatility

time-series model is simply a way to look at the
relationship between current and past values of
data. In the case of stock-return variance, a
time-series model would show how this month’s
variance is related to the variance of the stock
market over the past few months.*12 The best
long-run forecast of monthly stock-market vari­
ance is the variance calculated from a distribu­
tion like that in Figure l .13 But the best short-run
forecast of variance may be much lower or
higher, depending on what the variance has
been in recent months.
Economic theory suggests a method for fore­
casting stock-return variance: calculate the size
of past errors in forecasting stock returns,14
then use the squared values of these forecast
errors to estimate the stock-return variance.15

price, the risk-free interest rate, and the variance of the stock
price. Since the current price of the option is observed, the
Black-Scholes formula can be inverted to solve for the vari­
ance. This method o f calculating stock price variance is
referred to as the “im plied-volatility” method. See, for
example, the 1991 book Option Valuation: Analyzing and
Pricing Standardized Option Contracts, by Rajna Gibson. For
a comparison o f how well time-series methods and impliedvolatility methods characterize stock-return volatility, see
Day and Lewis (1992).
12Time-series modeling of variances is a very active area
o f research for economists. See the April/May (1992) issue
o f the Journal o f Econometrics, which is devoted entirely to
ARCH (autoregressive conditional heteroskedasticity) mod­
els o f financial market data. In their simplest form, ARCH
m odels assume that the current value o f the conditional
variance is a linear function o f past squared deviations.

D. Keith Sill

This method of forecasting stock-return vari­
ance makes intuitive sense as well. In calm
times, our forecasting model for stock returns
should predict relatively well, and so our fore­
cast error should be relatively small and the
predicted variance will be small. In a particu­
larly volatile time, our model will not fit quite
as well, so that the forecast error is large and the
predicted variance will be large.
We have plotted a measure of stock-market
volatility using forecast errors from a timeseries model of stock returns (Figure 2). The
figure shows the forecast errors from a fore­
casting model of monthly returns to the S&P
500 stock index from 1959 to 1992.16 Note that
the stock-market volatility measure shows a
great deal of variation. Volatility does not ap­
pear to be constant. The highest spike corre­
sponds to the month of October 1987. Recall
that on October 19, 1987, the stock market
experienced its sharpest one-day drop ever.
This figure also suggests a correlation through
time in return volatility. Visual evidence sug­
gests that sharp upward spikes are bunched
together. This pattern indicates that volatility
may in part be predictable based on its own past
values.
PREDICTING STOCK-MARKET
VOLATILITY
Why is it that stock-market volatility changes
over time? Are there regular patterns in the
time-series behavior of volatility? To help us
address these questions it is useful to have an
economic model of how stock prices are deter­
mined.

13We would need to calculate a distribution for monthly
stock returns. The distribution in Figure 1 is for annual stock
returns.
14This method o f calculating the variance and standard
deviation o f stock returns follows Schwert (1989) and Salinger
(1989). An alternative method is to calculate the variance of
daily stock returns and then use these daily variance obser­
vations to calculate a monthly variance. Schwert presents
graphical evidence indicating that the two m easures are
sim ilar.




15The forecast errors are the in-sample residuals from
the estimated model for returns. The variance that is esti­
mated from these forecast errors is called the conditional
variance of returns.
16The absolute value of each monthly forecast error is
plotted in Figure 2.

21

BUSINESS REVIEW

JANUARY/FEBRUARY 1993

FIGURE 2

Stock-Market Volatility
Absolute Value
of Forecast Error

Suppose we take a simple model that ex­
presses the current price of the stock as a
positive multiple of current dividend pay­
ments.17 This is certainly an oversimplification,
but it will keep the discussion uncomplicated.
For a stock portfolio as diversified as the S&P
500, current dividend payments might be
proxied by current, economywide output. If
the stock price is then represented as a positive
fraction of current output, the expected vari­
ance of stock returns will be positively related
to the expected variance of output growth.
In this model, the fundamental factor that
drives stock prices is the level of output. We

l7This result can be derived from an intertemporal
model o f asset pricing where investors face an uncertain
future and have utility that is a logarithmic function of
consumption. More general models of stock pricing sug­
gest that the current price o f a share of stock is related to the
entire future stream o f dividends that investors expect to
receive. See Sargent (1987) for a technical discussion of
these m odels.

Digitized for
22 FRASER


can think of output as indicating the state of the
economy. When output growth is high, the
state of the economy is good (expansions).
When output growth is low, the state of the
economy is bad (recessions). Any patterns over
time in the volatility of output growth will be
reflected in the volatility of stock returns. When
we examine output growth (as measured by
monthly industrial production), we find that
output-growth volatility is correlated over time
and that output-growth volatility is higher in
recessions than it is in expansions. Our simple
model suggests that we should see similar
behavior in the time path of stock-market vola­
tility.
Let us first examine whether stock-market
volatility is correlated through time. One way
to do this is by checking whether past volatility
is useful in predicting current volatility. If we
take monthly data on the S&P 500 from 1959 to
1992, we find that past volatility does help
predict future volatility. However, the model’s
ability to predict future volatility is rather poor.
Only a little over 1 percent of the total variation
FEDERAL RESERVE BANK OF PHILADELPHIA

Predicting Stock-Market Volatility

in return volatility is explained by its own past
values; over 98 percent of the movement over
time in volatility evident in Figure 2 remains
unexplained.
To test whether stock-market volatility is
higher in recessions than it is in expansions we
forecast volatility using data on its own past
values and a variable that captures whether the
economy is in a recession or an expansion. As
suggested by our model, we find that the reces­
sion variable does help to explain volatility.
Volatility is higher in recessions than in expan­
sions. Based on our volatility measure we
would forecast that the standard deviation of
monthly returns would rise by about 2 percent­
age points in recessions.18* By including the
recession variable in the volatility forecast equa­
tion we can account for about 6 percent of the
movement in stock-market volatility over time.
What other things might help us to improve
our predictions of volatility? What about the
seasons of the year? Is volatility predictably
higher in one month than in another? A simple
way to test for the presence of seasonal move­
ment in volatility is to form a forecast of vola­
tility using data on its own past values and a set
of variables that account for the different
months, or seasons, of the year. We can then
test whether these seasonal indicators improve
the forecast. Some evidence indicates that
stock-market volatility is predictably lower in
June, but in general, the evidence for a seasonal
pattern in stock-market volatility is weak.
What have we learned so far about patterns
in the behavior of stock-market volatility? First,
stock-market volatility is not constant. It can be
predicted, though rather imprecisely, using its
own past values. Second, volatility tends to be
higher in recessions than in expansions. Third,
there is weak evidence of a seasonal movement

18The long-run standard deviation o f m onthly stock
returns, measured by the S&P 500 index, is about 3.1 per­
cent.




D. Keith Sill

in volatility.
Prediction Using Macroeconomic Vari­
ables. We have seen that there are identifiable
patterns in stock-market volatility over time.
The observation that stock-market volatility is
higher in recessions than in expansions sug­
gests that we might improve forecasts of vola­
tility by using variables that predict recessions.
If we can predict recessions, perhaps we can
predict stock-market volatility. However, our
test will be a little more demanding. Stockmarket volatility itself predicts industrial-pro­
duction volatility and so might predict reces­
sions. Therefore, we will look at how well
macroeconomic variables forecast stock-mar­
ket volatility over and above the forecasting
power of past stock-market volatility itself.
I examined a battery of macroeconomic vari­
ables to see if they predict future stock-market
variability. These variables included inflation,
various measures of money-supply growth,
industrial production and consumer spending
growth, and oil price shocks. Somewhat sur­
prisingly, these macroeconomic variables did
not improve forecasts of stock-market volatil­
ity over and above forecasts made using past
levels of stock-market volatility. However,
interest-rate variables did help to improve pre­
dictions of volatility because interest rates con­
vey information about the risk of bankruptcy
and about the stance of monetary policy.
When a firm borrows money, it might go
bankrupt before paying off the loan. Lenders
realize this and charge an interest rate on loans
that reflects the firm’s default risk, which is the
likelihood that the firm will not pay off the loan.
Strong firms, which are unlikely to go bank­
rupt, pay low interest rates, while weak firms
pay higher interest rates. However, the whole
schedule of interest rates changes as the
economy changes. During recessions, all firms
face an increased risk of bankruptcy, so all
firms must pay higher interest rates on loans.
Since the chance of bankruptcy is higher in
recessions, expected dividend payments are
23

BUSINESS REVIEW

lower, and stock prices fall. Thus, there is a
correlation between the default risk on corpo­
rate borrowing and stock prices.
How can we measure default risk? One way
is to look at the interest rates on corporate
bonds and compare them with the interest rates
on default-free bonds, such as U.S. government
bonds. The difference between these two inter­
est rates, called an interest-rate spread, acts as
a measure of default risk.
A different interest-rate spread may provide
useful information about stock-market volatil­
ity in another way: the spread can indicate not
just default risk but also changes in monetary
policy. We have seen that stock-market volatil­
ity is higher in recessions than in expansions. If
tighter monetary policy predicts future reces­
sions, it will predict stock-market volatility. If
monetary policy tightens, the cost of funds to
banks increases. Banks will then have to in­
crease the interest rates they pay on certificates
of deposit (CDs). Since CDs and commercial
paper are near-perfect substitutes, their inter­
est rates will rise together; but Treasury bills are
imperfect substitutes for CDs, so their interest
rates won’t rise as much. The overall effect is
that the spread between interest rates on com­
mercial paper and Treasury bills will increase.
Another possibility is that banks may cut back
on loans to customers, but again, the spread
between commercial-paper interest rates and
Treasury-bill interest rates could rise. In this
case, firms issue commercial paper rather than
borrowing from banks, causing interest rates
on commercial paper to rise.19 If the spread
between the commercial-paper rate and the
Treasury-bill rate is a measure of the stance of
monetary policy, this spread could predict
stock-market volatility because it predicts fu­
ture recessions.

l9See Bemanke (1990) for an in-depth discussion of the
predictive power o f interest rates and interest-rate spreads
for future econom ic activity.

Digitized for
24FRASER


JANUARY/FEBRUARY 1993

Examining the data, we find that the inter­
est-rate spreads and their volatility help fore­
cast stock-market volatility. In both cases, the
default-premium variables have significant ex­
planatory power for stock-market volatility. In
fact, including the recession index and the
interest-rate spreads, we can account for about
10 percent of the variation in stock-market
v olatility.

The Time-Series Behavior of Expected Vola­
tility. The data show that stock-market volatil­
ity is difficult to predict. However, even though
forecasts of volatility might be poor, the eco­
nomic significance of these forecasts can be
large. Forecasts of stock-market volatility are a
measure of what people expect future stockmarket volatility to be. After all, a forecast is
just a best guess of what will happen in the
future. Recall from our discussion of people’s
spending and saving decisions and the discus­
sion of options prices that expected stock-mar­
ket volatility affects behavior and prices. People
act today based in part on their expectation of
future events. Therefore, we would like to
know if there are large changes over time in
expectations of future stock-market volatility.
We have plotted the forecasted, or expected,
stock-market volatility (Figure 3), constructed
using past values of stock-market volatility and
past values of the volatility of the interest-ratespread variable.20* Expected stock-market vola­
tility clearly changes through time, though the
movement is not as pronounced as the move­
ment in the volatility displayed in Figure 2.
(Recall that Figure 2 shows realized values of
the forecast errors.) The sharpest upward
movement in expected volatility occurs over
the period 1973 to 1975, which coincides with

20This measure o f expected volatility was constructed
by using a bivariate ARCH model for stock returns and the
T-bill/com m ercial paper spread. For details on how the
measure of expected stock-market volatility was constructed,
see my working paper listed in the References.

FEDERAL RESERVE BANK OF PHILADELPHIA

Predicting Stock-Market Volatility

D. Keith Sill

FIGURE 3

Expected Volatility of Stock
Expected Standard
Deviation of Returns

the first OPEC oil price shocks and a recession.
The next sharpest upward movement in ex­
pected volatility occurs in 1980, which also
coincides with a recession. In fact, expected
stock-market volatility in Figure 3 rises in each
of the six recessions since 1959.21
How econom ically significant are these
movements in expected volatility? Consider
the case of option prices. The Chicago Board
Options Exchange trades in call and put op­
tions on the S&P 500 index. Suppose that the
current level of the S&P 500 index is 426.65, the
call option contract has 30 days until maturity,
and the strike price of the option is $425. Sup­
pose further that the expected volatility of the
index return is 3.1 percent. Under these condi­
tions, the Black-Scholes option pricing formula

21The recessions occurred April 1960 to February 1961,
December 1969 to November 1970, November 1973 to March
1975, January 1980 to July 1980, July 1981 to November
1982, and, most recently, the recession that began in July
1990.




predicts that the price of
the call option is $6.87.22
Suppose that we keep all
Returns
parameters the same ex­
cept for the volatility of
returns, which increases
by 2 percentage points,
the amount that monthly
volatility is predicted to
increase during reces­
sions. In this case, the
Black-Scholes model pre­
dicts the call option price
will be $10.19. Thus, the
option price is quite sen­
sitive to changes in ex­
pected volatility. Eco­
nomic theory suggests
that changes in expected
volatility can also influ­
ence other economic
variables such as consumption and investment.
Measuring the effects of these changes in vola­
tility is an active area of research for econo­
mists.23
VOLATILITY IN THE 1980s
The data on stock-market volatility have
suggested that: (1) past levels of volatility pre­
dict future levels of volatility; (2) interest-rate
spreads help to predict volatility; and (3) vola­
tility is higher in recessions than expansions.
However, if we test propositions (1) and (2)
using data from 1980 through 1991, we find
little evidence to support them. That is, in the

22The parameters o f the B lack-Scholes pricing model
include the time to maturity of the contract, the current price
of the stock, the strike price of the contract, the volatility of
the stock return, and the value of the risk-free interest rate.
In the example in the text, the risk-free interest rate was
assumed to be 4 percent per year.
23For a comprehensive survey of recent empirical work
on time-series modeling of expected volatility, see Bollerslev,
Chou, and Kroner (1992).

25

BUSINESS REVIEW

1980s, the forecasting power of past levels of
stock-market volatility and the interest-rate
spread deteriorated significantly. Why was
this the case?
One possibility, suggested by the simple
model of stock pricing, is that the time-series
behavior of the volatility of output growth
changed in the 1980s. However, when the data
are examined we find that past values of out­
put-growth volatility still have predictive power
for future output-growth volatility in the 1980s.
According to the simple model, past levels of
stock-market volatility should still have pre­
dictive power for future volatility.
The change in the behavior of stock-market
volatility may be related to developments in
financial markets that occurred over the course
of the 1980s. For example, the transaction costs
of buying and selling stocks were much lower
in the 1980s than in the early 1970s. Institutions,
which account for about 80 percent of the trad­
ing on the New York Stock Exchange (NYSE),
now pay less than 5 cents per share in commis­
sions versus 80 cents per share in the early
1970s. These lower commission charges are
reflected in the increased volume of trading on
the market. This higher trading volume serves
to make the stock market more liquid, thus
helping to further reduce the costs associated
with executing a trade. With these lower costs
of trading, investors are able to react more
quickly and more frequently to new informa­
tion. These developments may have altered the
time-series behavior of volatility.
Another possibility is that the time-series
behavior of stock-market volatility has been
influenced by the trend toward increasing inte­
gration of world financial markets. In the
1960s, transactions by foreigners accounted for
about 12 percent of the dollar volume of trade
on the NYSE.24 In the 1970s the average had
risen to about 16 percent. In the 1980s, the
average reached over 19 percent. With the
increasing interdependence of world markets,
U.S. stock prices are influenced more and more



JANUARY/FEBRUARY 1993

by developments in foreign countries. This
could contribute to a change in the time-series
behavior of stock-market volatility.25
Why did the interest-rate variables have
lower forecasting power in the 1980s? In a 1990
article, Ben Bernanke offers two possibilities.
First, in the decade of the 1980s there have been
changes in the way the Federal Reserve imple­
ments its monetary policy. These changes
allowed short-term interest rates, such as the
federal funds rate, to become more variable, all
else equal. As a result, short-term interest rates
may have become less tightly linked to the
monetary policy actions that ultimately affect
the economy.
A second possibility is that financial deregu­
lation and financial innovation in the 1980s may
have increased the substitutability between
Treasury bills, commercial paper, and CDs. If
these assets are closer substitutes, the sensitiv­
ity of interest-rate spreads to changes in mon­
etary policy may be reduced. The weaker link
between interest-rate spreads and monetary
policy might then be reflected in a weaker link
between the interest-rate spreads and the
economy.
CONCLUSION
The data on stock returns suggest that: (1)
stock-market volatility can be predicted based
on its own past values; (2) volatility is higher in
recessions than in expansions; (3) some vari­
ables that theory suggests might help explain

24The percent of transactions accounted for by foreign­
ers is measured as the sum of sales by foreigners to Ameri­
cans and sales to foreigners by Americans divided by the
dollar volume of trade on the NYSE. These data are taken
from various issues of the New York Stock Exchange Fact Book.
25Another innovation to financial markets in the 1980s
has been the introduction of futures and options trading on
stock market indexes. These contracts allow investors to
buy and sell large baskets of stocks at a fraction of the cost
required to execute the same trade in the stock market.

FEDERAL RESERVE BANK OF PHILADELPHIA

Predicting Stock-Market Volatility

stock-market volatility (such as money-supply
variability, inflation variability, and industrialproduction variability) are not helpful; and (4)
the spread between commercial-paper rates
and Treasury-bill rates has predictive power
for stock-market volatility. However, the best
we can do with these variables is to explain
about 10 percent of the variation in stockmarket volatility over time. In addition, it
appears that volatility became more difficult to
predict in the 1980s.




D. Keith Sill

Even though it is difficult to accurately pre­
dict stock-market volatility, the forecasts that
people make about volatility are important.
Economic theory argues that it is these expecta­
tions about future volatility that can affect
people’s decisions to spend and save. Changes
in expected volatility can also affect stock prices
and investment and the prices of stock options.
The evidence suggests that there are substan­
tial movements in expected stock-market vola­
tility relative to the average level of volatility.

27

REFERENCES

BUSINESS REVIEW

JANUARY/FEBRUARY 1993

Abel, Andrew. “Stock Prices Under Time-Varying Dividend Risk: An Exact Solution in an
Infinite-Horizon General Equilibrium Model,” Journal o f Monetary Economics, 22 (1988),
pp. 375-93.
Barsky, Robert. “Why Don’t the Prices of Stocks and Bonds Move Together?” American
Economic Review, 79 (December 1989), pp. 1132-45.
Bemanke, Ben. “On the Predictive Power of Interest Rates and Interest Rate Spreads,” New
England Economic Review, (Nov/Dec 1990), pp. 51-68.
Black, Fisher, and Myron Scholes. “The Pricing of Options and Corporate Liabilities,”
Journal o f Political Economy, 81 (May/June 1973), pp. 637-59.
Blanchard, Olivier, and Stanley Fisher. Lectures on Macroeconomics. Cambridge: MIT Press,
1987.
Bollerslev, Tim, Ray Chou, and Kenneth Kroner. “ARCH Modeling in Finance: A Review of
the Theory and Empirical Evidence,” Journal o f Econometrics, 52 (April/May 1992),
pp 5-59.
Day, Theodore, and Craig Lewis. “Stock Market Volatility and the Information Content of
Stock Index Options,” Journal o f Econometrics, 52 (April/May 1992), pp. 267-87.
Fama, Eugene. “The Behavior of Stock Market Prices,” Journal o f Business, 38 (January 1965),
pp. 34-105.
Gibson, Rajna. Option Valuation: Analyzing and Pricing Standardized Option Contracts. New
York: McGraw-Hill, 1991.
Salinger, Michael. “Stock Market Margin Requirements and Volatility: Implications for
Regulation of Stock Index Futures,” Journal o f Financial Services Research, 3 (1989),
pp. 121-38.
Sargent, Thomas. Dynamic Macroeconomic Theory. Cambridge: Harvard University Press,
1987.
Schwert, G. William. “Why Does Stock Market Volatility Change Over Time,” Journal of
Finance, 44 (December 1989), pp. 1115-53.
Sill, D. Keith. “Stock-Return Volatility,” Federal Reserve Bank of Philadelphia Working
Paper (1993).


28


FEDERAL RESERVE BANK OF PHILADELPHIA

Philadelphia/RESEARCH
Working Papers
The Philadelphia Fed’s Research Department occasionally publishes working papers based on the current
research of staff economists. These papers, dealing with virtually all areas within economics and finance,
are intended for the professional researcher. The papers added to the Working Papers series in 1991 and
1992 are listed below. To order copies, please send the number of the item desired, along with your address,
to WORKING PAPERS, Department of Research, Federal Reserve Bank of Philadelphia, 10 Independence
Mall, Philadelphia, PA 19106. For overseas airmail requests only, a $2.00 per copy prepayment is required;
please make checks or money orders payable (in U.S. funds) to the Federal Reserve Bank of Philadelphia.
A list of all available papers may be ordered from the same address.
1991
No. 91-1

Dean Croushore, “A Measure of Federal Reserve Credibility.”

No. 91-2

James J. McAndrews and Leonard I. Nakamura, “Worker Debt With Bankruptcy.”

No. 91-3

William Lang and Leonard I. Nakamura, “Housing Appraisals and Redlining.”

No. 91-4

Paul S. Calem and John A. Rizzo, “Financing Constraints and Investment: New Evidence from
the U.S. Hospital Industry.”

No. 91-5

Paul S. Calem, “Reputation Acquisition, Collateral, and Moral Hazard in Debt Markets.”
(Superseded by No. 92-12)

No. 91-6

Loretta J. Mester, “Expense Preference and the Fed Revisited.”
(Superseded by No. 92-4)

No. 91-7

Choon-Geol Moon and Janet G. Stotsky, “The Effect of Rent Control on Housing Quality
Change: A Longitudinal Analysis.”

No. 91-8

Dean Croushore, “The Short-Run Costs of Disinflation.”

No. 91-9

Leonard I. Nakamura, “Delegated Monitoring With Diseconomies of Scale.”

No. 91-10

Sherrill Shaffer, “Forecast Announcements and Locally Persistent Bias.”
(Supersedes No. 89-6)

No. 91-11

Francis X. Diebold, Glenn D. Rudebusch, and Daniel E. Sichel, “Further Evidence on Business
Cycle Duration Dependence.”

No. 91-12

Sherrill Shaffer and James DiSalvo, “Conduct in Banking Duopoly.”

No. 91-13

William T. Bogart and Richard Voith, “Property Taxes, Homeownership Capitalization Rates,
and Housing Consumption.”

No. 91-14

Sherrill Shaffer, “Efficient Two-Part Tariffs With Uncertainty and Interdependent Demand.”
(Supersedes No. 88-18)




29

Philadelphia/RESEARCH
Working Papers
No. 91-15

Leonard I. Nakamura and Sherrill Shaffer, “Optimal Acceptance Policies for Journals.”

No. 91-16

Gerald Carlino, Richard Voith, and Brian Cody, “The Effects of Exchange Rate and
Productivity Changes on U.S. Industrial Output at the State Level.”

No. 91-17/R Sherrill Shaffer, “Can Megamergers Reduce Bank Costs?” (Supersedes “Potential Merger
Synergies Among Large Commercial Banks”)
No. 91-18

Gerald Carlino and Leonard Mills, “Have Regional Per-Capita Incomes Converged?”

No. 91-19

Richard Voith, “Changing Capitalization of CBD-Oriented Transportation Systems: Evidence
From Philadelphia, 1970-1988.”

No. 91-20

John Boschen and Leonard Mills, “The Effects of Countercyclical Monetary Policy on Money
and Interest Rates: An Evaluation of Evidence From FOMC Documents.”

No. 91-21

Joseph P. Hughes and Loretta Mester, “A Quality and Risk-Adjusted Cost Function for Banks:
Evidence on the “Too-Big-To-Fail” Doctrine.”

No. 91-22

Satyajit Chatterjee, “The Effect of Transitional Dynamics on the Distribution of Wealth in a
Neoclassical Capital Accumulation Model.”

No. 91-23

Satyajit Chatterjee and B. Ravikumar, “A Neoclassical Model of Seasonal Fluctuations.”

No. 91-24

George Mailath and Loretta Mester, “When Do Regulators Close Banks? When Should They?”
1992

No. 92-1

Leonard Nakamura, “Commercial Bank Information: Implications for the Structure of
Banking.”

No. 92-2

Shaghil Ahmed and Dean Croushore, “The Marginal Cost of Funds With Nonspecific Public
Spending.”

No. 92-3

Paul S. Calem, “The Delaware Valley Mortgage Plan: An Analysis Using HMDA Data.”

No. 92-4

Loretta J. Mester, “Further Evidence Concerning Expense Preference and the Fed.”
(Supersedes No. 91-6)

No. 92-5

Paul S. Calem, “The Location and Quality Effects of Mergers.”

No. 92-6

Dean Croushore, “Ricardian Equivalence Under Income Uncertainty.”
(Supersedes No. 90-8)

No. 92-7

James J. McAndrews, “Results of a Survey of ATM Network Pricing.”

No. 92-8

Loretta J. Mester, “Perpetual Signaling With Imperfectly Correlated Costs.”




FEDERAL RESERVE BANK OF PHILADELPHIA

Philadelphia/RESEARCH
Working Papers
No. 92-9

Mitchell Berlin and Loretta J. Mester, “Debt Covenants and Renegotiation.”

No. 92-10

Joseph Gyourko and Richard Voith, “Leasing as a Lottery: Implications for Rational
Building Surges and Increasing Vacancies.”

No. 92-11

Sherrill Shaffer, “A Revenue-Restricted Cost Study of 100 Large Banks.” (Supersedes FRBNY
Research Paper No. 8806)

No. 92-12

Paul Calem, “Reputation Acquisition and Persistence of Moral Hazard in Credit Markets.”
(Supersedes No. 91-5)

No. 92-13

Sherrill Shaffer, “Structure, Conduct, Performance, and Welfare” (Supersedes No. 90-27)

No. 92-14

Loretta J. Mester, “Efficiency in the Savings and Loan Industry.”

No. 92-15

Dean Croushore and Shaghil Ahmed, “The Importance of the Tax System in Determining the
Marginal Cost of Funds.”

No. 92-16

Keith Sill, “An Empirical Investigation of Money Demand in the Cash-in-Advance Model
Fram ew ork.”

No. 92-17

Sherrill Shaffer, “Optimal Linear Taxation of Polluting Firms.”

No. 92-18

Leonard I. Nakamura and Bruno M. Parigi, “Bank Branching.”

No. 92-19

Theodore M. Crone, Sherry Delaney, and Leonard O. Mills, “Vector-Autoregression Forecast
Models for the Third District States.”

No. 92-20

William W. Lang and Leonard I. Nakamura, ‘“ Flight to Quality’” in Bank Lending and
Economic Activity.”

No. 92-21

Theodore M. Crone and Richard P. Voith, “Estimating House Price Appreciation: A Compari­
son of Methods.”

No. 92-22

Shaghil Ahmed and Jae Ha Park, “Sources of Macroeconomic Fluctuations in Small
Economies.”

No. 92-23

Sherrill Shaffer, “A Note on Antitrust in a Stochastic Market.”

No. 92-24

Paul S. Calem and Loretta J. Mester, “Search, Switching Costs, and the Stickiness of Credit
Card Interest Rates.”

No. 92-25

Gregory P. Hopper, “Can a Time-Varying Risk Premium Explain the Failure of Uncovered
Interest Parity in the Market for Foreign Exchange?”

No. 92-26

Herb Taylor, “PSTAR+: A Small Macro Model for Policymakers.”




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RESERVE BANK OF
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