When the representative bank’s backward-bending loan supply curve peaks at its profit-maximizing loan rate, credit rationing can be an equilibrium phenomenon, which makes credit-dependen
Trang 1THE MOMENTUM TRADING STRATEGY
K.C JOHN WEI, Hong Kong University of Science and Technology, Hong Kong
Abstract
A strategy that buys past winners and simultaneously
sells past losers based on stock performance in the
past 3 to 12 months is profitable in the U.S and
the European markets This survey paper reviews
the literature on the momentum strategy and the
possible explanations on the momentum profitability
Keywords: past winners; past losers; momentum
strategy; individual momentum; industrial
momen-tum; international momenmomen-tum; underreaction;
over-reaction; overconfidence; self-attribution; valuation
uncertainty; conservatism; representative heuristic;
gradual information diffusion
45.1 Introduction
‘‘Trend is your friend’’ is a very popular saying in
Wall Street since the inception of stock markets
However, whether this momentum trading strategy
that is based on buying past winners and selling
past losers is really profitable was controversial
until recently Jegadeesh and Titman (1993) were
the first to comprehensively test the profitability of
the momentum trading strategy based on the past
3-to 12-month performance They document that
momentum strategies implemented in the U.S
market from 1965 to 1989 generated a positive
profit of about one percent per month over 3-to
12-month holding periods In their recent
follow-up study, Jegadeesh and Titman (2001) find that
momentum strategies continued to be profitable
after 1990 with past winners outperforming past losers by about the same magnitude as in the earl-ier period
Rouwenhorst (1998) studied individual stock momentum with a sample of stocks listed on 12 European exchanges during the period from 1978
to 1995 The results demonstrate that momentum profits of about one percent per month are not limited to a particular market, but instead they are present in all 12 markets in the sample Rou-wenhorst (1999) also finds that momentum strat-egies are profitable although not to the same degree in 20 emerging markets Chui et al (2002) examine the profitability of momentum strategies
in eight different Asian countries: Hong Kong, Indonesia, Japan, Korea, Malaysia, Singapore, Taiwan, and Thailand Their evidence indicates that the momentum effect is present in all of the Asian countries except Korea and Indonesia but it
is generally weak and is statistically significant only for Hong Kong, Malaysia, Singapore, and Thailand for the pre-crisis period Interestingly, they find that the Common Law=Civil Law dis-tinction provides an indicator of whether or not a market exhibited a momentum effect prior to the financial crisis Asness et al (1996), Chan et al (2000), and Richards (1997) document that mo-mentum strategies are profitable when implemen-ted on stock market indices
Recently Moskowitz and Grinblatt (1999) find that industry momentum strategies, which advocate buying stocks from past winning indus-tries and selling stocks from past losing indusindus-tries,
Trang 2appear to be highly profitable This industry
mo-mentum accounts for much of the profitability of
individual stock momentum strategies in the
United States Once returns are adjusted for
indus-try effects, momentum profits from individual
equities are significantly weaker, and for the
most part are statistically insignificant However,
Grundy and Martin (2001) have a different view
on the contribution of industries to individual
momentum profits They argue that a one-month
interval between the ranking period and the
hold-ing period has a pivotal role in the conclusion that
industry momentum strategies are profitable
In-dustry momentum strategies are significantly
prof-itable only when the ranking period is contiguous
to the holding period as documented by
Mosko-witz and Grinblatt (1999) However, given a
one-month interval between the two periods, industry
momentum strategies cannot earn significant
profits Grundy and Martin (2001) conclude that
industry effects are not the primary cause of the
individual momentum profitability Liu and Wei
(2004) document that industries in 12 European
markets, like their counterparts in the U.S market,
also explain the profitability of individual
momen-tum strategies Specifically, past winner industries
outperform past loser industries by more than one
percent per month However, unlike their
counter-parts in the U.S market, industries cannot solely
explain the profitability of individual momentum
strategies in 12 European markets In addition,
industry momentum strategies can still earn
sig-nificant profits even with a one-month interval
between the formation and holding periods
45.2 The Implementation of Momentum Strategies
To show how to implement a momentum strategy,
we use a momentum strategy that is based on the
past six-month performance with a six-month
holding period an illustration Specifically, to
form momentum portfolios, at the end of each
month all securities in each of the samples are
ranked in ascending order based on the past
six-month cumulative returns with dividends The
securities in the bottom 10 percent (or 20 percent
or 30 percent) are assigned to the loser (denoted as
‘‘L’’) portfolio, while those in the top 10 percent (or 20 percent or 30 percent) are assigned to the winner (denoted as ‘‘W’’) portfolio These portfo-lios are value-weighted using the market capital-ization of the security at the end of the ranking month as the weight Each of these portfolios is held for six months
To reduce the effect of nonsynchronous trading and the bid–ask bounce, Jegadeesh and Titman (1993) suggest that we measure returns on these portfolios one month after the ranking takes place
If a security has any missing returns during the holding period, we replace them with the corre-sponding value-weighted market returns If the returns on the security are no longer available, we rebalance the portfolio in the month the security is deleted from our database Excess returns on a security are calculated as the returns on that secur-ity minus the risk-free rate, which we assume is equal to the one-month government short-term rate, such as the U.S Treasury bill rate
To increase the power of our tests, we construct overlapping portfolios The winner (loser) port-folio is an overlapping portport-folio that consists of the ‘‘W’’ (‘‘L’’) portfolios in the previous six months The returns on the winner (loser) portfo-lios are the simple average of the returns on the six
‘‘W’’ (‘‘L’’) portfolios For instance, the January return on the winner portfolio is the simple average
of the January returns on the ‘‘W’’ portfolios that are constructed from June to November in the previous year The momentum portfolio we exam-ine is the zero-cost, winner-minus-loser portfolio
45.3 Explanations of Momentum Profits Jegadeesh and Titman (2001) discuss three poten-tial explanations for the profitability of momen-tum strategies and examine the performance of momentum portfolios over longer horizons in order to differentiate between these hypotheses The three explanations include: (1) stock prices underreact to information, (2) there is a delayed
Trang 3overreaction to information, and (3) the profits are
generated from cross-sectional differences in
expected returns
The first two explanations are consistent with
some recent behavioral models For example,
the underreaction explanation is consistent with
the Barberis, Shleifer, and Vishny (1998) model
where a ‘‘conservatism bias’’ can lead investors
to underreact or underweight new information
In the case with a pure conservatism bias, once
the information is fully incorporated in prices,
there is no predictability in stock returns In this
case, the expected post-holding period returns
are zero
There are a number of behavioral models that
are consistent with a delayed overreaction
Bar-beris et al (1998) also discuss this possibility and
describe what they call the ‘‘representative
heuris-tic,’’ which suggests that investors may overly
ex-trapolate a firm’s past extraordinary earning
growths into the future, and hence overreact to
positive (or negative) information that is preceded
by positive (or negative) information In addition,
Daniel et al (1998) argue that delayed overreaction
can arise because of ‘‘self-attribution (or cognitive)
bias.’’ That is, investors tend to become more
overconfident when their stock picks become
win-ners and take more aggressive positions that push
up the prices of winners above their fundamental
values Finally, Hong and Stein (1999) propose a
model with two groups of investors: informed
in-vestors and technical traders, who do not fully take
into account the actions of each other As a result,
information is incorporated slowly into stock
prices, providing a potential profit opportunity
for technical traders These traders, however, tend
to push prices of past winners above their
funda-mental values In each of these behavioral models,
prices tend to eventually overreact to information
and then reverse when prices eventually revert to
their fundamentals All these behavioral models
predict the expected post-holding period returns
to be negative
The third explanation is consistent with an
effi-cient market where stocks have different expected
rates of return because of different risk exposures
In particular, Conrad and Kaul (1998) emphasize that there would be some evidence of momen-tum even if there were no time-series variation
in expected returns since stocks with high-(low) expected returns would be expected to have the highest (lowest) returns in adjacent periods This explanation suggests that the profits from a mo-mentum strategy should be the same in any post-ranking period
To test these competing hypotheses, we normally examine the post-holding period returns of momen-tum portfolios beyond the first year after formation, typically up to five years The empirical evidence from the U.S (Jegadeesh and Titman, 2001) and Asian markets (Chui et al., 2002) appears to support the delayed overreaction explanation That is, the returns on the momentum portfolio eventually reverse to negative 2–5 years after formation In addition, Fama and French (1996) find that the Fama–French (1993) three factors cannot explain the momentum profits in the United States
45.4 Momentum Profits and Firm Characteristics Firm characteristics such as book-to-market ra-tios, market capitalization, and turnover have shown to have the ability to predict the cross sec-tion of expected stock returns in the United States Behavioral models also predict that momentum profits are related to firm characteristics
The overconfidence model by Daniel, Hirshleifer, and Subrahmanyam (1998) suggests that momen-tum profits arise because investors are overconfi-dence Daniel and Titman (1999) argue that overconfidence is likely to influence the perception
of investors relatively more, when they analyze fairly vague and subjective information, and use book-to-market ratios as a proxy for information vagueness Consistent with their hypothesis, they find that mo-mentum profits are negatively related to the firm’s book-to-market ratio in the U.S market Chui et al (2002) also find similar results for Asian markets Trading volume or turnover could also proxy for information vagueness As suggested by
Trang 4asym-metric information models (see for example,
Blume et al., 1994), trading volume reflects
inves-tors’ disagreement on a stock’s intrinsic value The
more vague the information used to value the firm,
the more disagreement among the investors, and
hence, the greater the trading volume Therefore,
the momentum effect should be stronger for firms
with high trading volume or turnover Lee and
Swaminathan (2000) find that momentum profits
are indeed higher for firms with high turnover
ratios in the U.S market Chui et al (2002) also
find similar results for Asian markets
In contrast, Hong and Stein (1999) predict that
stocks with slow information diffusion should
ex-hibit stronger momentum Hong et al (2000)
pro-vide tests that support this prediction In
particular, except for the very smallest decile
stocks, the profitability of momentum investment
strategies declines sharply with firm size Hong
et al (2000) also look at momentum profits and
analyst coverage and find that holding size
fixed-momentum strategies work better for stock with
low analyst coverage In addition, they find that
the effect of analyst coverage is greater for stocks
that are past losers than for stocks that are past
winners They conclude that their findings are
con-sistent with the gradual information diffusion
model of Hong and Stein (1999)
Acknowledgment
The author would like to acknowledge financial
support from the Research Grants Council of the
Hong Kong Special Administration Region, China
(HKUST6233=97H)
REFERENCES Asness, C.S., Liew, J.M., and Stevens, R.L (1996).
‘‘Parallels between the cross-sectional predictability
of stock returns and country returns.’’ Working
Paper, Goldman Sachs Asset Management.
Barberis, N., Shleifer, A., and Vishny, R (1998) ‘‘A
model of investor sentiment.’’ Journal of Financial
Economics, 49: 307–343.
Blume, L., Easley, D., and O’Hara, M (1994) ‘‘Market statistics and technical analysis: The role of volume.’’ Journal of Finance, 49: 153–181.
Chan, K., Hameed, A., and Tong, W (2000) ‘‘Profit-ability of momentum strategies in the international equity markets.’’ Journal of Financial and Quantita-tive Analysis, 35: 153–172.
Chui, A.C.W., Titman, S., and Wei, K.C.J (2002).
‘‘Momentum, legal system, and ownership structure:
an analysis of Asian stock markets.’’ Working Paper, University of Texas at Austin.
Conrad, J and Kaul, G (1998) ‘‘An anatomy of trading strategies.’’ Review of Financial Studies, 11: 489–519 Daniel, K.D and Titman, S (1999) ‘‘Market efficiency
in an irrational world.’’ Financial Analysts Journal, 55: 28–40.
Daniel, K., Hirshleifer, D., and Subrahmanyam, A (1998) ‘‘Investor psychology and security market under-and overreactions.’’ Journal of Finance, 53: 1839–1886.
Fama, E.F and French, K.R (1993) ‘‘Common risk factors in the returns on stocks and bonds.’’ Journal
of Financial Economics, 33: 3–56.
Fama, E and French, K (1996) ‘‘Multifactor explan-ations of asset pricing anomalies,’’ Journal of Fi-nance, 51: 55–84.
Grundy, B.D., and Martin J.S (2001) ‘‘Understanding the nature of the risks and the source of the rewards
to mementum investing,’’ Review of Financial Stud-ies, 14: 29–78.
Hong, H and Stein, J.C (1999) ‘‘A unified theory of underreaction, momentum trading and overreaction
in asset markets.’’ Journal of Finance, 54: 2143– 2184.
Hong, H., Lim, T and Stein, J.C (2000) ‘‘Bad news travels slowly: size, analyst coverage, and the profit-ability of momentum strategies.’’ Journal of Finance, 55: 265–295.
Jegadeesh, N and Titman, S (1993) ‘‘Returns to buy-ing winners and sellbuy-ing losers: Implications for stock market efficiency.’’ Journal of Finance, 48: 65–91.
Jegadeesh, N and Titman, S (2001) ‘‘Profitability of momentum strategies: an evaluation of alternative explanations.’’ Journal of Finance, 56: 699–720 Lee, C.M.C and Swaminathan, B (2000) ‘‘Price mo-mentum and trading volume.’’ Journal of Finance, 55: 2017–2069.
Liu, S and Wei, K.C.J (2004) ‘‘Do industries explain the profitability of momentum strategies in Euro-pean markets?’’ Working Paper, Hong Kong Uni-versity of Science and Technology.
Trang 5Lu, C and Shen, Y (2005) ‘‘Do REITs pay enough
dividends?’’ Unpublished working paper,
Depart-ment of Finance, Yuan Ze University.
Moskowitz, T.J and Grinblatt, M (1999) ‘‘Do
indus-tries explain momentum?’’ Journal of Finance, 54:
1249–1290.
Richards, A.J (1997) ‘‘Winner-loser reversals in
na-tional stock market indices: Can they be explained?’’
Journal of Finance, 52: 2129–2144.
Rouwenhorst, K.G (1998) ‘‘International momentum strategies.’’ Journal of Finance, 53: 267–284.
Rouwenhorst, K.G (1999) ‘‘Local return factors and turnover in emerging stock markets,’’ Journal of Fi-nance, 55: 1439–1464.
Trang 6Chapter 46
EQUILIBRIUM CREDIT RATIONING AND
MONETARY NONNEUTRALITY IN
A SMALL OPEN ECONOMY
YING WU, Salisbury University, USA
Abstract
This paper modifies the well-known
Mundell–Flem-ing model by addMundell–Flem-ing equilibrium credit rationMundell–Flem-ing as
well as imperfect asset substitutability between
bonds and loans When the representative bank’s
backward-bending loan supply curve peaks at its
profit-maximizing loan rate, credit rationing can be
an equilibrium phenomenon, which makes
credit-dependent capital investment solely credit-dependent upon
the availability of customer market credit With
credit rationing, an expansion in money and credit
shifts the IS curve as well as the LM curve even in a
small open economy under a regime of fixed
ex-change rates, and the magnitude of offset coefficient
between domestic and foreign asset components of
high-powered money is less than one In contrast,
if there is no credit rationing, imperfect asset
substitutability between bonds and loans per se
can-not generate the real effect of money in the same
model
JEL classification: E51 F41
Keywords: credit rationing; monetary policy;
capital flow; Mundell–Fleming model; monetary
neutrality; open market operation; IS-LM
curves; offset coefficient; monetary base; small
open economy
46.1 Introduction
Is money non-neutral in a small open economy with international capital mobility and a fixed exchange rate regime? Can monetary policy affect real output
in these circumstances? The answer to these ques-tions is widely construed to be negative because the money supply has lost its role of a nominal anchor
in this case.1In the orthodox money view, it is the interest rate that serves as the channel through which monetary policy affects the real sector of an economy; however, because the interest rate chan-nel of monetary policy is highly correlated with exchange rates, and because the monetary authority commits to the maintenance of the fixed exchange rate, the consequent foreign exchange intervention
by the monetary authority using official reserves necessarily washes out any real effect of the monet-ary policy that it has previously initiated The same approach is used in most of the existing literature
on small open economies, such as the traditional IS=LM analysis, which holds a lopsided view of bank liabilities and bank loans Other than influen-cing interest rates via manipulating deposits (a money asset and bank liability), banks have no active leverage to play with; the role of bank loans escapes unnoticed since bank loans are grouped together with other nonmonetary assets such as bonds
Trang 7In contrast to the money view, the credit view of
monetary transmission mechanism rejects the
no-tion that all nonmonetary assets are perfect
sub-stitutes According to the credit view, due to
information asymmetries between borrowers and
lenders in financial markets, banks can play a
par-ticular role in reducing information costs It is
financial intermediation that can help a firm with
risk-sharing, liquidity, and information services; as
a result, a large number of firms have in fact
be-come bank dependent Furthermore, although a
rise in the loan rate increases, ceteris paribus, the
bank’s expected return by increasing interest
pay-ment when the borrower does not default, it lowers
the bank’s expected return by exacerbating
adverse selection and moral hazard problems,
and thus raising the probability of default Hence,
the bank’s loan supply curve can be
backward-bending, and credit rationing may occur as an
equilibrium phenomenon.2 Credit rationing per se
makes monetary credit availability rather than
interest rates in order to be the conduit for the
real effect of money, therefore providing a major
theoretical underpinning for the effectiveness of
monetary policy under fixed exchange rates
This paper begins with a study of the loan market
setting with asymmetric information as a
micro-foundation for consumption and investment, and
further develops a macromodel of a small open
economy under a fixed exchange rate regime with
perfect capital mobility in the bond market and
imperfect asset substitutability between bonds and
loans As far as the credit view is concerned, this
paper in spirit is close to Bernanke and Blinder
(1988), who address the credit channel of monetary
policy in a variant of the IS=LM model They differ
in several regards, however Unlike Bernanke and
Blinder, the model in this paper incorporates the
possibility of equilibrium credit rationing while
maintaining the assumption of imperfect
substitut-ability of bank loans and bonds With imperfect
substitutability between bonds and bank loans,
this paper nests both rationed and
credit-unrationed equilibrium regimes Additionally, by
placing the credit channel of monetary policy in
the setting of a small open economy, this chapter allows the possibility to explore the relevance of the ‘‘monetary policy ineffectiveness’’ proposition
in the existing mainstream small-open-economy literature
Partly based on Wu (1999) by drawing on its microeconomic foundation setting, this study has made important and substantial revisions to its macroeconomic analysis With the credit availability channel, this study shows that money in the fixed exchange rate model is not completely endogenous
by appealing to the asymmetry between customer market credit and auction market credit under equi-librium credit rationing.3Incorporating bank credit into the fixed exchange rate model leads to two fundamental changes First, it extends the scope for monetary policy to affect economy from the stand-ard interest rate channel to the one including the bank lending channel and balance sheet channel as well; the latter two conduits can be independent of changes in interest rates Second, and more import-antly, monetary policy will no longer be deemed impotent since it can directly ‘‘shift’’ the goods mar-ket as well as money marmar-ket equilibrium schedules in such a way that the targeted real effect could be achieved while the fixed exchange rate is sustained The next section presents the analytical struc-ture of bank behavior and credit market; the fol-lowing two sections explore how credit market conditions determine macroeconomic equilibrium
in an open-economy IS=LM framework, and dem-onstrate the real impacts of monetary shocks through its credit channel, respectively The final section concludes the study
46.2 Bank Behavior and Credit Market
It is well known that due to the credit risk associ-ated with adverse selection and moral hazard prob-lems a banking firm has an inverse U-shaped loan supply curve with a backward-bending portion This section essentially modifies the pedagogical model in Christopher and Lewarne (1994) by extending the spectrum of bank investment into the portfolio selection between bonds and loans
Trang 8The representative banking firm is assumed to
hold exactly the required amount of reserves, and
allocate all of its excess reserves between the two
bank assets: bonds and loans Thus, it chooses
loans, l, subject to its balance sheet identity, to
maximize its profits from lending
P ¼ u(r)lr þ bbr dr g
2l
2
s:t: bbþ l ¼ (1 k)d,
(46:1)
where r is the loan rate, u(r) the probability of
loan repayment, g the cost parameter of
ser-vicing loans, b b denotes bonds held by the
banking firm, r is the interest rate on bond, d
represents total deposits, and k is the required
reserve ratio for deposits.
Here, the low-risk or risk-free interest rate on
bond holding is assumed to be the same as the
interest cost of taking in deposits Thus, deposits
and bonds are perfectly substitutable assets to
depositors so that they pay the same expected
return per dollar The key characteristic of the
bank profit is that the repayment probability
depends on the loan rate Following the existing
literature on equilibrium credit rationing, an
in-crease in the loan rate makes it more likely for
borrowers to default, hence the repayment
prob-ability is a decreasing function of the loan rate.4
In addition, the representative bank takes the
flow of deposits as given when making its
port-folio decisions Substituting the balance sheet
identity into the bank’s objective function and
maximizing it with respect to l yields the banking
firm’s loan supply curve
lS ¼u(r)r r
Several implications of the loan supply curve can
be derived First, the loan supply curve is
back-ward bending The co-movement of the loan rate
and loan volume hinges on the elasticity of the
odds of repayment with respect to the loan rate
Only when the repayment probability is inelastic
can a positive relationship exist between the loan
rate and loan volume To be specific, consider
a linear repayment probability u(r)¼ f cr,
where f is the autonomous repayment probabil-ity determined by noninterest factors such as the liquidity of balance sheet positions, and c meas-ures the sensitivity of the repayment probability
to the loan rate (0 < c < f 1) Figure 46.1
depicts the loan repayment probability function
In the case of linear loan repayment probability function, the loan volume supplied increases with the loan rate until the loan rate achieves f=2c, after which a higher loan rate actually reduces the loan volume In Figure 46.1, the loan rate at which the loan supply curve begins to bend back-ward points to the repayment probability halfway
to its maximum within the possible range Substituting u(r)¼ f cr into (46.2) and
dif-ferentiating (46.2) with respect to r,f, c, and g produces the responses of loan supply to the parameters of servicing loans In particular, an increase in the bond interest rate, r, ceteris par-ibus, makes bond holding more attractive; ac-cordingly, banks will reduce loans and hold more bonds Another interpretation for the de-crease of bank loans is based on the equivalence between the bond interest rate and the deposit rate: the higher the interest expenses of raising loanable funds by issuing deposits, the higher the economic cost of making loans Next, banks tend
to issue more loans when the autonomous repay-ment probability, f, is higher, for example due to borrowers’ increased net worth, In addition, the larger the sensitivity of the repayment probabil-ity to the loan interest rate, c, the more deteri-orating the problems of adverse selection and moral hazard, thus it is more likely for credit rationing to occur Finally, an increase in the cost of servicing loans, g, also tends to reduce loans as long as the expected return per dollar of loans exceeds the corresponding real opportunity cost
Applying the envelope theorem to the representa-tive bank’s profit function in Equation (46.1) while incorporating Equation (46.2) and u(r)¼ f cr
generates the following marginal bank profit with respect to the loan rate:
EQUILIBRIUM CREDIT RATIONING AND MONETARY NONNEUTRALITY IN A SMALL OPEN ECONOMY 707
Trang 9dr ¼1
g[2c
2r3 3cfr2
þ (2cr þ f2)r fr]:
(46:3)
The bracket term on the RHS of Equation (46.3) is
a cubic expression but two of the three roots are
degenerated solutions at which loans are zero,
re-spectively; thus the only feasible root for Equation
(46.3) is r ¼ f=2c, at which the bank’s expected
profits are maximized Recall that the bank’s loan
supply curve peaks exactly at the same loan rate as
the profit-maximizing loan rate here Therefore,
the result suggests the existence of equilibrium
credit rationing Further, the result for
profit-maximizing loans also imply that the loan interest
rate exceeds the bond interest rate such that
r > ffiffiffiffiffiffiffiffi
r=c
p
> r, which captures the existence of
risk premium of bank lending, and therefore
signi-fies the imperfect substitutability between loans
and bonds
Moving from the representative bank to the
aggregate banking system, the aggregated bank
balance sheet identity shows Bbþ L þ R ¼ D,
where Bb represents the bonds held by banks, D
denotes deposits, and L is the volume of loans
For simplicity, currency is abstracted from the
model The required reserve of the banking
sys-tem, R, constitutes the monetary authority’s
li-abilities, or high-powered money, H, which are
generated by its acquisition of bonds (Ba) and foreign exchange (F ) The high-powered money
in this framework is composed of exclusively required reserves; the money supply can be ex-pressed by H=k
Suppose there are n banks, with the representa-tive bank’s supply of loans specified in Equation (46.2) aggregating, and which generates the total supply of loans A structural view of the aggre-gated balance sheet of banks suggests that if banks allocate a fraction of their excess reserves into loans and the rest into bonds, the aggregate supply of loans is given by «(1 k) (H=k), where
« represents the ratio of loans to excess reserves Accordingly, the share of loans in excess reserves must characterize the banks’ loan-making behav-ior and it is thus actually a function of the same set
of variables that determine aggregate supply of loans
LS ¼ «(r, r, f, c, g, n) 1 k
k
H,
? þ þ
(46:4)
where the symbols underneath each of the
argu-ments in «(.) denote the signs of the partial
deriva-tives associated with them For simplicity, it is assumed that bank credit is the only debt instru-ment for firms to finance their investinstru-ment; invest-ment demand and the demand for bank loans are taken to be equal.5 Thus, aggregate demand for
q(r)
r
f
2
f
2 Ψ
Ψ Figure 46.1 Loan repayment probability
Trang 10loans is negatively related to the loan interest rate,
and its standard linear form is
Indeed, as demonstrated by the existing literature
on markets in disequilibrium, the loan market may
or may not be at the market-clearing equilibrium.6
Nevertheless, unlike disequilibrium economics, the
loan quantity traded in the market is not uniformly
characterized by the minimum of demand and
sup-ply sides Loan rationing can arise in an
unre-stricted market setting flawed only by plausible
information asymmetries; the loan rate can always
freely adjust to a level consistent with market forces
driven by the profit-maximization incentives
Therefore, credit rationing could exist at the
profit-maximizing loan rate, r ¼ f=2c, and
sus-tain as an equilibrium phenomenon The excess
demand fails to drive the loan rate upward because
the associated credit risk would reduce banks’
profits; however, if at the same loan rate there is
an excess supply, the loan interest rate will adjust
downward to clear the loan market, since holding
excess reserves does not add to profits at all
Consider the demand for and supply of loans
specified in Equations (46.4) and (46.5),
respect-ively, then the equilibrium interest rate in the loan
market is given by
r¼
f
2c, if L
D LS at f
2c; min (r1, r2jLD¼ LS ), if LD< LSat f
2c,
8
>
>
(46:6) where r1and r2are the two roots of the quadratic
equation given by LD ¼ LS Recall that r ¼ f=2c
is the loan rate that corresponds to the maximum
quantity of loans If an excess supply exists at
r, LD must cross LS once at a loan rate below r
and once at a loan rate above r Since r is the
profit-maximizing loan rate, the bank has no
in-centive to raise the loan rate to any level above r,
and credit is then rationed at the equilibrium On
the other hand, the profit-maximizing loan rate is
not attainable if there is excess supply at r, since
the bank cannot force the firms to borrow in excess
of the amount that maximizes their profits It follows that if a bank cannot maximize its profit
at r due to deficient demand, the best attainable outcome for the bank is to allow a downward adjustment in the loan rate until the loan market clears Therefore, the loan quantity traded is at the market-clearing equilibrium level if the market interest rate of loans is below the banks’ desired level, r; otherwise, it would be determined by supply at the profit-maximizing loan rate
46.3 Macroeconomic Equilibrium Assume that investment is solely dependent on the availability of bank credit, and investment demand
is equivalent to the demand for loans Based on the analytical results in the preceding section, there is
an implicit positive relationship between the inter-est rates on loans and bonds, which can be expli-citly expressed as r¼ l(r) If credit demand is
not rationed in the loan market, we have I(r) LD[l(r)], with I0 ¼ L 0Dl0<0, however, with credit rationing, investment demand is totally determined by the aggregate supply of loans 46.3.1 Case for Credit Rationing
With credit rationing, the quantity of loans effect-ively traded is given by LSas specified in Equation (46.4) In this case, the monetary authority can help loosen credit rationing through open market purchases: the nonbank public, which sells bonds
to the monetary authority deposits the proceeds into banks, and the loan supply increases with the deposits The rationing situation improves and the resulting increase in output increases money demand, and thus imposes upward pressure on the interest rate and the exchange value of the domestic currency This in turn relieves the money market of the adjustment burden resulting from the monetary authority’s commitment to the fixed exchange rate under the circumstances of open market purchases Therefore, following the monetary authority’s open market purchases,
EQUILIBRIUM CREDIT RATIONING AND MONETARY NONNEUTRALITY IN A SMALL OPEN ECONOMY 709