NON-INTEREST INCOME AND U.S. BANK STOCK

RETURNS∗

Jason AllenDepartment of Economics, Queen’s University

Job Market Paper

January 10, 2005

Abstract

This paper investigates U.S. bank common stock returns and their sensitivity to market risk, interest

rate risk, and illiquidity risk. Due to known problems with conducting inference using Generalized

Method of Moments, I use the Empirical Likelihood Block Bootstrap established in Allen, Gregory,

and Shimotsu (2004). Preliminary results suggest that once the test-statistics are bootstrapped, ag-

gregate illiquidity is not a significant factor in explaining U.S. bank stock returns. However, bank

stock returns are sensitive to illiquidity in the commercial paper market. There is also evidence of a

negative relationship between market capitalization and sensitivity to illiquidity in the commercial

paper market.

Keywords: U.S. banks, Liquidity and Asset pricing, Empirical likelihood, Block bootstrap

JEL classification: G21, G12, C12

∗Correspondence: Department of Economics, Queen’s University, Kingston, ON, Canada, K7L 3N6. e-mail: al-lenj@qed.econ.queensu.ca. Preliminary draft, please do not cite. I am grateful for the advice and guidance of Allan Gregory.I also thank Kim Huynh, Jeremy Lise, Darcey Mcvanel, Katsumi Shimotsu, Gregor Smith, and participants in the Queen’sQuantitative workshop and PhD seminar series. Thanks to Kristin Stone for providing the FDIC data. Funding by the Queen’sInstitute for Economic Research is gratefully appreciated. All errors are my own.

1 Introduction

In the past twenty years there has been a transformation of U.S. banking from primarily providing

loans to providing services in a wide range of activities. Many of these activities are off-balance sheet,

generating non-interest income. These activities have contributed substantially to bank profits. The

focus of this paper is on loan commitments, instruments which generate considerable non-interest in-

come. A loan commitment is a note by a bank to a firm promising to lend up to a fixed amount at a fixed

or variable rate when the option is in the money and the firm profits at the banks expense. The purpose

of this paper is to examine the relationship between this phenomenon and changes in risks associated

with U.S. bank stock returns. Investors worry about risk because it affects their overall welfare. If the

banking industry is taking on risk associated with unused loan commitments, investors will demand to

be rewarded with higher expected returns. Due to the special nature of banking, regulators also face the

challenge of determining the appropriate risk factors faced by banks. Regulators are concerned with the

role of banks in providing liquidity and facilitating the monetary transmission mechanism. Regulators

are also concerned with the role of deposit insurance, which can provide banks with the incentive to

take on excessive risk, which may lead to financial instability. I estimate by generalized method of mo-

ments a linear multifactor model, taking into account market risk, interest rate risk, aggregate illiquidity

risk, and risk associated with unused loan commitments. Due to the well-known finite sample problems

with generalized method of moments, the analysis applies the block bootstrap procedure described in

Allen, Gregory, and Shimotsu (2004) to conduct inference.

The trend in banking towards non-interest income was first reported by Boyd and Gertler (1994).

Figure 1 shows the share on non-interest income as a fraction of net operating revenue increasing from

28 percent to 42 percent in just the last twenty years. Taking off-balance sheet (OBS) activities into

account, Boyd and Gertler (1994) show that contrary to previous research, U.S. commercial banking

is still very important to the U.S. economy. In the stochastic frontier literature, adding non-interest

income as a banks output increases measures of economies of scale and efficiency. Examples include

papers by Clark and Siems (2002) and Allen and Liu (2004).

Complementary research during the 1980s and early 1990s used multifactor models to explain stock

movements of U.S. financial institutions. Building on earlier work by Flannery and James (1984) and

Yourougou (1990), Song (1994) tests a two factor model for U.S. commercial banking stock returns.

Song finds that between 1976 and 1987 market risk and interest rate risk are key factors in explaining

stock returns and that these risks are time-varying. Bank lending may be interest rate sensitive because

of the different maturities in banks assets and liabilities. Flannery and James (1984) coined the term

mismatch hypothesisand found it to hold for U.S. commercial banks and unexpected interest rate

changes.1 Neuberger (1991) uses similar data from 1979 to 1990 and concludes that there has been

1The idea that interest rate risk may help explain stock returns dates back to Stone (1974).

1

a shift in sensitivities of bank stocks to risk factors. The author finds that U.S. common stocks are

less sensitive to interest rate risk in the latter part of the sample. I find that interest rate risk is not a

significant factor in explaining U.S. bank stock returns for the period 1984 to 2003.

The documented decrease in interest rate sensitivity of U.S. financial institutions stock returns may

be due to the shift towards loan commitments as a major source of revenue. Figure 2 presents un-

used loan commitments as a fraction of unused loan commitments plus loans/leases for national banks

covered by the Federal Depository Institution Corporation (FDIC) for the period 1984 to 2003. Un-

used commitments have increased from under twenty percent to over fifty percent in the past twenty

years. This number is greater if you take into account locked interest rate loan commitments, which

are officially classified as derivatives. Loan commitments present large profits for banks by generating

significant non-interest income. Deyoung and Rice (2004) point out that non-interest income is impor-

tant for both small and large financial institutions, although more so for banks with greater than one

billion dollars in assets. One can think of two competing arguments. First, since large banks have a

larger proportion of their operating revenue in non-interest income they should be more sensitive to un-

expected changes in the sources of non-interest income. The competing argument is “flight to size” and

diversification. Large banks benefit from economies of scale in providing off-balance sheet services

and are therefore better suited for these unexpected changes. I thus include large and small institutions

and experiment with the effect of size on stock returns.

Trading income and fees related to trading income are volatile components of off-balance sheet

activities. Therefore, like non-financial firms, banks are susceptible to aggregate illiquidity risk. Illiq-

uidity is an unobservable variable representing the cost of selling. I proxy aggregate illiquidity risk in a

manner similar to Amihud (2002). Illiquidity is defined as the change in price with respect to a change

in volume. Hasbrouck (2002) finds the Amihud measure most closely proxies the high-frequency trade

and quote data that would be ideal to use if the time series of high-frequency data was more substan-

tial. Amihud (2002) finds a positive relationship between expected illiquidity and stocks returns on the

NYSE and a negative relationship between those returns with unexpected illiquidity. This relationship

is also shown to be stronger with smaller stocks. I test these hypotheses for U.S. bank stock returns.

I find a negative relationship between unexpected aggregate illiquidity and stock returns, however the

relationship is not significant once I correct for the poor inference common in method of moments

estimation.

More consequential for the banking industry, illiquidity in the commercial paper market constitutes

a plausible source of risk. U.S. banks are involved in the commercial paper market through loan com-

mitments. Kashyap, Rajan, and Stein (2002) construct a model where banks are best suited to cushion

illiquidity in the commercial paper market because demand deposits are positively but not perfectly

correlated with illiquidity in this market. Gatev and Strahan (2004) provide some empirical evidence

of this. Therefore, expected illiquidity risk should be priced and unexpected illiquidity should have a

2

negative impact on a banks stocks price. Given a return, investors do not want to hold stocks that are

strongly correlated with illiquidity. I find some evidence that U.S. bank stock returns are sensitive to

illiquidity in the commercial paper market.

Unused loan commitments to firms floating commercial paper represents a risk specific to banking.

A loan commitment is a promissory note by a bank to lend in the future to a firm up to a pre-specified

amount at a pre-specified rate.2 A loan commitment therefore acts like an option where a firm exercises

the loan when the commitment’s interest rate is below the market rate. In most cases, the market is for

high-grade commercial paper (CP). Figure 3 shows the nominal increase in unused loan commitments

from 1984 to 2003. The story is the following: from 1984 to 2001 bank lending has fallen because

highly rated borrowers have secured an increasing amount of credit in the commercial paper market.

Figure 5 shows the sharp increase from a approximately $47 billion to $360 billion. The figure also

shows the sharp drop in CP issued beginning in 2001. Reasons for this decline include an overall eco-

nomic downturn, possible debt restructuring caused by a flattening of the yield curve, and heightened

investor fear. The latter has come because of an increase in default and default ratings. There is also

a move toward asset-backed CP. Even in strong economic conditions, CP issuers typically secure a

backup line of credit from one or more banks to protect themselves from illiquidity in the CP market.

The amount of unused loan commitments increased from 525 million to 4 billion 1992 dollars in the

past twenty years.

I estimate a linear multifactor model by least squares and generalized method of moments (GMM).

By increasing the number of orthogonality conditions, GMM estimates will be more precise than least

squares. It is well known that tests based on GMM estimates behave poorly in cases where data are

serially correlated (see the 1996 special issue of the Journal of Business Economics & Statistics). Un-

expected shocks should not be serially correlated. However, people typically apply simple rules for

interest rate processes or expected illiquidity. These rules generally do not capture all of the dynamics

in the data but are considered “good enough,” and then apply GMM, which only requires weak dis-

tributional assumptions. Hall and Horowitz (1996) propose a non-overlapping block bootstrap (NBB)

approach to get critical values and conduct inference. Inoue and Shintani (2001) use an overlapping

block bootstrap for linear GMM. They assume a more general form of dependence than Hall and

Horowitz (1996) and reduce the approximation error of the test statistics. I propose to use the empirical

likelihood block bootstrap (ELB) in conjunction with GMM to conduct better inference. This allows

for the case of serially uncorrelated shocks where the block size equals one. Derivation of the ELB and

it’s efficiency is presented in Allen, Gregory, and Shimotsu (2004). The procedure is similar to? for

iid processes. The main advantage of the ELB over the NBB is that the moment conditions hold under

the null and alternative hypotheses.

2Holmstrom and Tirole (1998) provide arguments for why firms will sign loan commitment contracts.

3

The paper is organized as follows. Section 2 presents the model. Section 3 provides a detailed

review of the data. Section 4 presents the interesting methodological issues and section 5 discusses

results. Section 6 concludes. Figures and tables are relegated to the appendix.

2 Model

The model is presented as follows:

r jt = β j0 +β jMMt +β jI It +β jLAILAt +β jLBILB

t + ε jt j = 1, ...,5. (1)

wherer j is the excess return on the one-month Treasury bill rate (from Ibbotson Associates) of portfolio

j; M is the excess market return and is proxied by the value-weighted return on the NYSE/AMEX minus

the one-month Treasury bill;I is the measure of unexpected interest rate risk on a government bond;

ILA is unexpected aggregate illiquidity risk; andILB is banks-specific unexpected illiquidity risk.3

If the market is efficient only the unexpected components of the factors should impact stock re-

turns. Expected changes would be incorporated in the price of the stocks. Unexpected interest rate risk

is the residual of an autoregressive process of the actual interest rate. I experiment with two proxies for

the interest rate, the total return on a long-term government bond (R1) and intermediate-term govern-

ment bond (R2), both provided by Ibbotson Associates. The interest rates are highly correlated, with

coefficient 0.89. Interest rates are assumed to follow an AR(1) process:

RiI ,t = α0 +α1Ri

I ,t−1 +uit ,

and I it = ui

t for i = 1,2, the long-term government bond and intermediate-term government bond,

respectively. Estimating a vector autoregression of order one for the interest rate series gives near

identical results.

Similarly I construct unexpected illiquidity risk as the residual from an autoregressive process in the

levels of illiquidity. Aggregate illiquidity is measured as the ratio of absolute monthly returns averaged

across daily observations,Ridt , and the dollar volume traded,VOLDidt , of stocki on the NYSE/AMEX

3I also consider the spread between the Certificate of Deposit (CD) rate and the one-month Treasury bill. This is sometimesused to proxy the risk of bank default (e.g. Hannan and Hanweck (1988) and Isimbabi and Tucker (1997)). In no experimentconsidered do I find a significant premium. For parsimony I do not discuss the CD rate any further.

4

(Center for Research in Stock Prices sharecodes 10 and 11) at timet. The measure is based on Amihud

(2002) and is meant to capture the impact of a price change per dollar of trading volume:4

LAit =

1Dit

Dit

∑d=1

|Ridt |VOLDidt

i = 1, ...,N; t = 1, ...,T. (2)

Aggregate illiquidity is the cross-sectional average of individual illiquidity measures, adjusted for

the growth trend of the NYSE/AMEX:

LAt =

mt

m1

1N

N

∑i=1

LAit , (3)

wherem1 is the total value of the stocks traded in January 1980 andmt is the total value of the stocks

traded at timet. Several conditions are required for a stock to be included in the aggregate measure of

illiquidity. First, the price of the stock must be greater than $5 and less than $1000 at the beginning

of the month. Second, a stock must have at least 15 daily observations at montht (Dit ). The final step

before aggregating across firms is to remove the one percent of illiquidity measures in each tail of the

distribution of illiquidity. This is to remove the “outlier” observations. These criterion are standard in

the literature.

Banks-specific illiquidity risk is proxied with the spread between the 3-month commercial paper

rate for highly rated financial borrowers and the 3-month Treasury bill rate (series H.15 provided by

the Federal Reserve Board of Governors):

LBt = CPt −TBILLt (4)

3 Banking Data

The sample of bank returns is constructed using monthly data provided by the Center for Research

in Security Prices (CRSP) at the University of Chicago. The sample spans January 1980 to December

2003 and all commercial banks, savings institutions (collectively labeled deposit institutions), security

and commodity brokers/dealers/exchanges/services (collectively labeled brokers), and Bank Holding

companies (BHC), with at least five years of trading are included. There are a total of 479 deposit

institutions, 179 brokers, and 85 BHCs. Financial institutions will be divided into portfolios based on

Fama and MacBeth (1973) to make estimation manageable.

4I am experimenting with other measures of aggregate illiquidity but results are preliminary.

5

Unlike previous studies I include several types of financial institutions, not simply commercial

banks. The Gramm-Leach-Bliley Act (GLBA) legislated in 1999 changed many of the rules in U.S.

banking, blurring the line between commercial banking and investment banking. Rules pertaining to

BHCs were also relaxed. Barth, Jr., and Wilcox (2000) provide a good discussion of the GLBA. I

include the aforementioned institutions and allow entry and exit when defining portfolios. Financial

institutions are classified by a permanent number (PERMNO). In the case of mergers, the acquiring

company remains in the sample and the acquired company is removed on the date of the acquisition.

Note that any names changes are irrelevant. Given a PERMNO, I can follow a firm through multiple

name changes. Occasionally a firm is classified under two categories. For example, the Bank of Hawaii

is classified as a deposit institution and a bank holding company and Westcorp is classified as a deposit

institution and a broker. Clearly, a firm is only included once. I am not differentiating between types,

therefore it is inconsequential to which category a firm is classified.

I adopt the familiar portfolio-based approach to sorting stocks. Sorting stocks based on particular

characteristics increases the cross-sectional dispersion in returns. This makes it easier to test whether

or not the sorting factor is important in determining stock returns.

The five portfolios are based on two separate criteria. First stocks are sorted by sensitivity, to

banks-specific illiquidity risk and second by size. A large number of financial institutions are included

ex ante because of the large number of mergers, takeovers, and attrition rate. Previous research on

U.S. bank stock returns includes only banks whose data is available for the whole sample. These leads

to a survivorship bias that is not present in the current portfolio-based approach. To be included in a

portfolio a stock must meet several criteria. First, only stocks with at least four years of consecutive

data are included. The sorting is based on least squares regressions, therefore an adequate number of

observations is required. Every December (t) from 1984 to 2002 I take the previous four years of each

firm and if there is no missing data the firm is included in the portfolio process. Given a firm is in the

sample att, stock returns are regressed on risk factors:

Ri,t = γi0 + γiMMt + γi2It + γi3ILAt + γi4ILB

t +ηi,t i = 1, ...,N. (5)

The portfolio returns are then sorted for the following twelve months by their sensitivity to banks-

specific illiquidity risk (γi4). I do this for t = 48, ...,T and construct a string of postranking equal-

weighted returns for each portfolio for 1985 to 2003. Note that this procedure allows a stocks beta to

be time-varying, and moving across portfolios. Consider JP Morgan Chase (formerly Chase Manhattan

Corp., formerly Chemical Banking Corp., and formerly Chemical New York Corp.), whose portfolio

rankings are plotted across time in figure 6. A procedure which does not take into account time-

variation in the parameters would clearly be inadequate in this situation. JP Morgan visits each portfolio

at least once, spending as much time in low sensitivity portfolios as high sensitivity ones.

6

The same method is used to sort portfolios by size, except there is no estimation. At timet, given a

stock has four years of data, the returns are used to construct five portfolios sorted by market capitaliza-

tion in December of that year. Market capitalization is defined as the product of shares outstanding and

stock price and is a common indicator of size. Deyoung and Rice (2004) find that larger banks (with

assets greater than $1 billion) rely on non-interest income more than smaller banks (with assets less

than $1 billion). This leads to a testable hypothesis - are larger banks. I find that this is not the case,

that in fact there is a flight to size story. That is, when there is an illiquidity shock in the commercial

paper market, larger banks are less risky than smaller banks.

Table 1 and 2 report time series means, standard deviations and sample autocorrelations of key

factors. These statistics help us define unexpected interest rate risk and unexpected illiquidity risk.

The mean excess return on the market is0.63%and there is very little serial correlation. The interest

rate terms are highly correlated although the intermediate-term bond rate is much more volatile. The

bank-specific illiquidity measure has mean0.58%with significant persistence. However, a unit root is

rejected. The hypothesis that aggregate illiquidity has a unit root is also rejected. The average change

in price per dollar of volume exchanged, i.e. aggregate illiquidity, is 0.5210.

The correlation between the commercial paper (CP) spread and the interest rates are positive, and

strongest between the spread and the intermediate-term bond return. Commercial paper acts like a

short-term loan therefore this is not surprising. The positive correlation implies that as the price of

borrowing increases firms exercise their option with banks. If liquidity dries up in the CP market then

banks are left exposed to these options, lending at interest rates below the market interest rate. There are

two risks for banks. First, that too many firms will exercise their option to borrow. This is especially

worrisome given the herding behavior with respect to liquidity (see Vayanos (2004)). Second, the

lending rate is firm-specific which generates a moral hazard problem.

Portmanteau tests of the risk measures are presented in table 3. Aggregate illiquidity risk is calcu-

lated as the residual of autoregressions of order three. The same is done for banks-specific illiquidity.

For estimation, the important ingredient for these generated regressors is no serial correlation. I test

for serial correlation (Ljung-Box Q-stat), normality (Jacque-Bera test), and autoregressive conditional

heteroscedasticity (ARCH). The null hypothesis of no serial correlation is not rejected at typical levels.

Normality is rejected and there is evidence of ARCH.

Figure 7 and 8 present time series for the long-term and intermediate-term interest rate risks, re-

spectively. The long-term interest rate risk is slightly more volatile than the intermediate-term interest

rate risk. Figure 9 and 10 present time series for banks-specific illiquidity and aggregate illiquidity,

respectively. The fluctuations in banks-specific illiquidity are very small, although there is some het-

eroscedasticity. For aggregate illiquidity there is greater volatility at the beginning relative to the end

of the sample.

7

Table 1: Descriptive Statistics: Factors, 1980:1-2003:12

Illiquidity InterestM LA LB R1

I R2I

Mean 0.0063 0.5210 0.0058 0.0090 0.0077Std. dev. 0.2190 0.0672 0.0043 0.0328 0.0178

sacf at lag 1 0.0406 0.7741 0.8181 0.0654 0.20722 -0.0541 0.6967 0.6654 -0.0630 -0.11563 -0.0526 0.6645 0.5351 -0.0947 -0.06274 -0.0794 0.6177 0.4427 -0.0088 -0.04455 0.0926 0.6220 0.4454 0.0528 -0.0179

Note: M is the return on NYSE/AMEX in excess of the one-month Treasury bill;LA is aggregateilliquidity; LB is the CP-tbill spread;R1

I is the return on the long-term government bond; andR2I is

the return on the intermediate-term government bond. sacf is the sample autocorrelation function.

Table 2: Descriptive Statistics: Correlations

Illiquidity InterestM LA LB R1

I R2I

M 1.0 -0.0784 -0.0595 0.2124 0.1488LA 1.0 -0.2216 0.0008 0.0023LB 1.0 0.1486 0.2091R1

I 1.0 0.8902R2

I 1.0

Note: M is the return on NYSE/AMEX in excess of the one-month Treasury bill;LA is ag-gregate illiquidity;LB is the CP-tbill spread;R1

I is the return on the long-term governmentbond; andR2

I is the return on the intermediate-term government bond.

Table 3: Portmanteau Tests, 1980:1-2003:12

I1 I2 ILA ILB

Ljung-Box Q Test 4.88 9.18 9.10 12.12(0.5593) (0.1635) (0.1683) (0.0594)

Jacque-Bera Test 48.87 395 301 1017(0.0000) (0.0000) (0.0000) (0.0000)

ARCH Test 20.98 29.11 32.79 43.83(0.0019) (0.0000) (0.0000) (0.0000)

Note: I1 is the intermediate-term interest rate risk factor;I2 is the long-term interest raterisk factor; ILA is aggregate illiquidity risk; andILB is banks-specific illiquidity risk. p-values are presented in parentheses for the null hypothesis of no serial correlation, normal-ity, and no ARCH.

8

Tables 4-6 present summary statistics for the five portfolios created by sorting by sensitivity to

banks-specific illiquidity and size. The most sensitive banks require higher returns, implying that

investors require compensation for investing in banks which are less liquid in the commercial paper

market. This is consistent with standard asset pricing theory, which says that investors will only take

on extra risk if they are adequately rewarded. In terms of sorting by size, table 6 shows there is no clear

evidence that smaller stocks have a lower rate return than larger stocks. The return onr1 is significantly

less than the other stocks but the returns are not monotonically increasing in size.

Table 4: Descriptive Statistics: portfolios sorted byβL 1984:1-2003:12

r j,t = γ j0 + γ jM Mt + γ jLAILAt + γ jLBILB

t + γ jI I1t +η j,t

r1 r2 r34 r4 r5

Mean 0.0154 0.0143 0.0127 0.0107 0.0102Std. dev. 0.0642 0.0520 0.0493 0.0495 0.0500

sacf at lag 1 0.2609 0.2372 0.1658 0.2211 0.14992 -0.0073 0.0366 0.0971 0.0571 0.04683 -0.0491 -0.0102 -0.0027 -0.0049 0.00654 -0.0916 -0.1317 -0.0684 -0.0580 -0.06655 0.0298 0.0616 0.0938 0.1101 0.02756

Note: portfolios (r) are ranked by sensitivity to illiquidity risk from most sensitive (r1)to least sensitive (r5). ILA is the unexpected aggregate illiquidity.ILB is the unexpectedbank-specific illiquidity risk;I1 is the unexpected interest risk proxied by the long-termgovernment bond. sacf is the sample autocorrelation function.

4 Method

4.1 Least Squares

The first focus is on the intercept coefficients estimated by regressing sorted portfolio returns on

different market factors. A significant intercept term (α) indicates that the factors do not explain the

variation in the portfolio returns. The joint hypothesis that the intercepts are zero is tested using the F-

test and the MacKinlay and Richardson (1991) method of moments based test (φ1). The latter is robust

to serial correlation and heteroscedasticity and has been shown to have better finite sample properties

than theF-test. TheF-test tends to under-reject a null hypothesis in finite samples. Theφ1-test is

defined as follows:

φ1 = (T−N−1)α′[R[D′TS−1

T DT ]−1R′]−1α ∼ χ2N , (6)

9

Table 5: Descriptive Statistics: portfolios sorted byβL 1984:1-2003:12

r j,t = γ j0 + γ jM Mt + γ jLAILAt + γ jLBILB

t + γ jI I2t +η j,t

r1 r2 r34 r4 r5

Mean 0.0152 0.0141 0.0131 0.0107 0.0102Std. dev. 0.0644 0.0516 0.0494 0.0492 0.050

sacf at lag 1 0.2737 0.2298 0.1820 0.2069 0.14352 -0.0111 0.0551 0.0517 0.0711 0.05053 -0.0415 -0.0003 -0.0203 0.0080 -0.00124 -0.0756 -0.1310 -0.0605 -0.0632 -0.06655 0.0233 0.0665 0.1071 0.0941 0.026

Note: portfolios (r) are ranked by sensitivity to illiquidity risk from most sensitive (r1)to least sensitive (r5). ILA is the unexpected aggregate illiquidity.ILB is the unexpectedbank-specific illiquidity risk;I2 is the unexpected interest risk proxied by the short-termgovernment bond. sacf is the sample autocorrelation function.

Table 6: Descriptive Statistics: portfolios sorted by size 1984:1-2003:12

r1 r2 r3 r4 r5

Mean 0.0060 0.0138 0.0161 0.0139 0.0135Std. dev. 0.0608 0.0474 0.0526 0.0542 0.0632

sacf at lag 1 0.2661 0.2614 0.2429 0.1204 0.03242 0.1044 0.0536 0.0601 0.0054 -0.00243 -0.0176 0.0418 -0.0773 0.0089 -0.04964 -0.0653 -0.0720 -0.0837 -0.0989 -0.08855 -0.0118 0.0214 0.0738 0.1264 0.1004

Note: portfolios (r) are ranked by market capitalization, from smallest (r1) to largest (r5).sacf is the sample autocorrelation function.

whereDT is the gradient,ST is the long run covariance matrix andR= [10]⊗ IN, whereN is the number

of assets and0 is a column vector which equals the number of regressors.

Portfolio α’s are presented for the illiquidity case in table 7 and 8. The most sensitive stocks have

a higherα and ther5− r1 spread is negative, indicating the portfolio most sensitive to illiquidity in

the commercial paper market pays the highest premium. The CAPM spread has anα of 5.04%, the

Fama-French spreadα is 5.28% per annum and the Four-Factorα is 6.12% per annum.

Table 9 presents theα’s for the portfolios sorted by size. Ther5− r1 spread is positive implying

larger banks have larger expected returns. The CAPMα for the spread is 4.92% per annum, the Fama-

Frenchα is 5.76% per annum and the Four-Factorα is 7.68% per annum. The test statistics for the

10

joint hypothesis that theα’s are zero is presented in table 10. Except for the strange case of the four-

factor model and portfolios sorted by size, theF-test is always less than the MR-test. Except for the

CAPM, the factor models explain the variation in the sorted portfolios. The least squares results suggest

illiquidity is not priced.

Table 7:α’s of portfolios sorted by illiquidityβ 1984:1-2003:12

r1 r2 r3 r4 r4 r5− r1

CAPM 0.0087 0.0080 0.0069 0.0047 0.0045 -0.0042(2.8162) (3.7007) (3.3170) (2.3438) (1.9966) (1.4824)

Fama-French 0.0075 0.0066 0.0056 0.0034 0.0032 -0.0044(2.9975) (3.6688) (3.0929) (1.8993) (1.5789) (1.7387)

Four-Factor 0.0061 0.0039 0.0029 0.0010 0.0010 -0.0051(2.3064) (1.9896) (1.5411) (0.5298) (0.4578) (1.8658)

Note: portfolios are ranked by sensitivity to illiquidity risk from most sensitive (r1) to least sensitive (r5).The portfolios include the long-term interest rate. Theαs are intercepts from least squares regressionsusing Newey and West (1987) HAC estimator on [M], [M,SMB,HML], and [M,SMB,HML,MOM] for theCAPM, Fame-French, and Four-Factor models, respectively. HAC robust t-statistics are in parentheses.

Table 8:α’s of portfolios sorted by illiquidityβ 1984:1-2003:12

r1 r2 r3 r4 r4 r5− r1

CAPM 0.0086 0.0077 0.0072 0.0049 0.0044 -0.0042(2.7187) (3.7678) (3.4734) 2.3863) (1.9621) (1.4675)

Fama-French 0.0073 0.0064 0.0060 0.0036 0.0030 -0.0042(2.8214) (3.7439) (3.3223) 1.9758) (1.5128) (1.6768)

Four-Factor 0.0057 0.0039 0.0032 0.0010 0.0012 -0.0045(2.1071) (2.0978) (1.6971) 0.4990) (0.5282) (1.6555)

Note: portfolios are ranked by sensitivity to illiquidity risk from most sensitive (r1) to least sensitive (r5).The portfolios include the intermediate-term interest rate. Theα’s are intercepts from least squares regres-sions using Newey and West (1987) HAC estimator on [M], [M,SMB,HML], and [M,SMB,HML,MOM]for the CAPM, Fame-French, and Four-Factor models, respectively. HAC robust t-statistics are in paren-theses.

11

Table 9:α’s of Portfolios sorted by size 1984:1-2003:12

r1 r2 r3 r4 r4 r5− r1

CAPM 0.0012 0.0090 0.0102 0.0073 0.0053 0.0041(0.3528) ( 3.9774) ( 4.2124) (3.3216) (2.4730) (1.1498)

Fama-French -0.0005 0.0077 0.0088 0.0060 0.0043 0.0048(-0.1788) (4.3423) (4.4672) (3.0125) ( 2.1020) (1.5756)

Four-Factor -0.0027 0.0054 0.0048 0.0038 0.0037 0.0064(0.9802) (2.5769) (2.2693) (1.7452) (1.6010) (2.0430)

Note: portfolios are ranked by size from smallest to largest Theα’s are intercepts from least squares regres-sions using Newey and West (1987) HAC estimator on [M], [M,SMB,HML], and [M,SMB,HML,MOM]for the CAPM, Fame-French, and Four-Factor models, respectively. HAC robust t-statistics are in paren-theses.

Table 10: Joint Hypothesis Testing

Sort by Illiquidity Sort by SizeF φ1 F φ1

CAPM 4.0852 12.182 5.8623 32.531(0.0444) (0.0324) (0.0162) (0.0000)

Fama-French 2.3920 9.726 4.2554 8.154(0.1233) (0.0834) (0.0402) (0.1479)

Four-Factor 0.2222 6.3697 2.8130 0.9931(0.6378) (0.2219) (0.2945) (0.9631)

Note: This table presents the F-test and Mackinlay-Richardson test (φ1) forthe joint hypothesis that the intercepts are zero. p-values are in parentheses.

12

4.2 Generalized Empirical Likelihood

The evidence on illiquidity risk is relatively weak using least squares methods. Therefore I esti-

mate the factor premiums jointly using GMM. GMM estimates will be more precise than least squares

because more information is used in the form of orthogonality conditions. However, it is also well

known that tests based on GMM behave poorly in finite sample. This is especially true when data are

serially correlated. Hall and Horowitz (1996) propose a block bootstrap approach to obtain critical

values and conduct inference. The block bootstrap divides time series data (Xt) into asymptotically

independent blocks. The non-overlapping blocks (Carlstein (1986)) used in this paper divide the data

into blocks of lengthl such thatB1 = {X1, ...,Xl}, B2 = {Xl+1, ...,X2l}, and so on. These blocks are

then resampled with replacement. See Hardle, Horowitz, and Kreiss (2001) for an excellent discussion

of bootstrapping time series.

Hall and Horowitz (1996) resample the blocks using a uniform distribution and calculate the boot-

strap critical values to conduct inference. Through Edgeworth expansion and Monte Carlo evidence, the

authors show that bootstrapping weakly dependent processes in this manner leads to better inference.

I propose a block bootstrap approach based on the multinomial distribution function. Given a GMM

estimate of the parameters it is possible to calculate probability weights from the empirical likelihood

estimator of Owen (1990). Rather than resample from the uniform distribution, I resample using these

probability weights. Asymptotically the probability weights approach the uniform distribution (with

weight 1/T). However, in finite samples these weights can be significantly different from1/T. The

ELB should work better than other block bootstrapping techniques because it imposes the moment

conditions exactly. There is more information from the multinomial probability weights than1/T,

therefore in some sense the bootstrap is more efficient. The ELB is also easier to implement than the

NBB suggested by Hall and Horowitz (1996), since, as we will see, there is no need to correct the

standard test statistics.

I first review GMM and empirical likelihood (EL) and then outline the bootstrapping method.

LetX = (x1, . . . ,xt), wherexi ∈Rk is ak×1 random variable, andt = 1, . . .T, be a set of observables

from a stationary sequence. Suppose for some true parameter-valueβ0 (k×1) the following moment

conditions (mequations) hold andm≥ k :

E [g(xt ,β0)] = 0 (7)

whereg : X×Θ→ Rg. This is, of course, the usual set-up for GMM and leads to the estimator:

βT = argminβ∈Θ

(T−1

T

∑t=1

g(xt ,β))′

WT

(T−1

T

∑t=1

g(xt ,β))

(8)

13

where the positive semi-definite weighting matrix,WT converges to a positive definite matrix of con-

stants. Hansen (1982) shows that the optimal weighting matrix converges toS−1T where

ST = ∑∞j=−∞ Egt(β0)gt− j(β0)′ ≡ Γ0 + ∑∞

j=0(Γ j + Γ′j). The t-test and Hansen (1982)’sJ-test are given

by:

TTr =T1/2(βTr−β0r)

((σT)rr )1/2∼ N(0,1) (9)

JT = TgT(βT)′S−1T gT(βT) ∼ χ2

m−k (10)

where(σT)rr is the covariance of parameterr, ST = ∑T−1j=−(T−1) k

( jh

)Γ j , h is the bandwidth, andk(·)

is a kernel function satisfyingk(x) = k(−x), k(0) = 1, and |k(x)| ≤ 1. Several kernels satisfy this

condition, including the usual Bartlett kernel. I use the Quadratic Spectral kernel because it offers

certain advantages over the Bartlett (Gotze and Kunsch (1996)).

The EL estimator uses the same moment conditions to solve a saddle point problem, maximizing the

objective function Ł(β,π) subject to the moment conditions holding exactly (Qin and Lawless (1994)).

I assume data is partitioned into Q blocks of lengthL. For the iid caseQ= T. The empirical likelihood

is

Ł(π1, ...,πq) =Q

∏q=1

πq

for 0≤ πq≤ 1, ∑Qq=1 πq = 1. The empirical likelihood estimator is the one that maximizes the following

equation:

argmaxβ,π

minδ∈∆(β)

Q

∑i=1

log(πq)+µ(1−Q

∑q=1

πq)−Qδ′Q

∑q=1

πqΓq(xt ,β) (11)

whereδ = (δ1,δ2, ...,δm)′ andΓq(β) = 1L ∑L

i=1g(x(i−1)L+i ,β). This equation is a highly dimensional

convex optimization problem and the solution has known drawbacks (see for example Gregory, Lamarche,

and Smith (2002)). However, in some instances there are advantages to estimatingβ by empirical likeli-

hood rather than GMM. For example, in nonlinear models the two-step GMM estimate of the weighting

matrix is often poorly estimated. EL sets the moment conditions to hold exactly and therefore I do not

estimate a weighting matrix. However, in our simple instrumental variables model I do not need to

solve the more complicated EL problem. Given the GMM estimate,β one can solve for the probability

weights,

πq =1Q

(1

1+ δ′Γq(x, β)

)(12)

where

δ = argminδ

[−T log(T)−∑ log(1+δ′Γ(xt , β))

]

The bootstrap can now be implemented with the probability weights to conduct better inference about

β than using asymptotic results. For the test statistics the formulas are identical for the bootstrap

14

sample as in the original data. This is note the case in Gotze and Kunsch (1996) or Hall and Horowitz

(1996). The authors use “corrected” statistics so that the Edgeworth expansions go through to give

asymptotic improvements over normality. Oddly, Goncalves and Vogelsang (2004) find that in their

particular Monte Carlo experiment using the standard formulas gives better results than Gotze and

Kunsch (1996).

The bootstrap procedure is the following:

1. Given the random samplex = (x1, ...,xT) calculateβ using 2-stage GMM

2. Set the block length equal to the width of the data-dependent lag window in estimating the long-

run covariance matrix (see Newey and West (1994))

3. Calculateπq using equation (12)

4. Sample with replacement fromx usingPr(x = x(i−1)L+i) = πq for i = 1, ...,L

5. CalculateJ and T using equations (10) and (9).

6. Repeat steps 3-5 B times

7. Let qπα be a(1−α) percentile of the distribution ofT

8. Letqπα be a(1−α) percentile of the distribution ofJ

9. The bootstrap confidence interval isβ j ± qπα√

(Vj j /T)

10. For the bootstrapJ-test, the test rejects ifJ≥ qπα

Setting the block length equal to the lag window width deserves further discussion. It is not neces-

sary to set the block lag length equal to the window width in the HAC estimator, although it is the norm

(Gotze and Kunsch (1996)). Inoue and Shintani (2001) use a general-to-specific approach similar to

Box-Jenkins analysis, which seems like a reasonable alternative, although not used here. When choos-

ing the truncation parameter using Newey and West (1994)’s data-dependent procedure the truncation

length can be quite large. If the number of parameters is also large the bootstrap breaks down. That is,

there is a rank condition that must be satisfied. Therefore the maximum block length allowed isT1/3.

15

5 Findings

Estimation and inference for the multifactor model by GMM follows the procedure outlined in

section 4.2. The sample moment conditions for the first-step GMM are:

g(xt ,β) =1T

T

∑t=1

ε jt ⊗zt , (13)

whereejt = Rj,t − (β j0 + β jMMt + β jLAILAt + β jLBILB

t + β jI I1t ), β = [β jo,β jM ,β jI ,βA

jIL ,βBjIL ], andzt =

[Mt , It , ILAt , ILB

t ,Mt−1, It−1, ILAt−1, IL

Bt−1]. The multinomial probability weights are given byπq in equa-

tion 12 where:

Γ(xt , β) =1Q

Q

∑q=1

ε jq⊗zq (14)

is the moment condition for the blocked data. Estimates and p-values are reported for portfolios sorted

by sensitivity to banks-specific illiquidity in tables 11 and 12.

The sign on the long-term interest rate risk is negative, however the coefficients are almost all

insignificant using asymptotic p-values and always insignificant using bootstrapped p-values. This

implies weak evidence of the mismatch hypothesis discussed in Flannery and James (1984) but similar

to results in Neuberger (1991). He found interest rate risk to be less explicative in the latter part of his

sample, and our evidence suggests this trend continues. An explanation may be the regulatory changes

undertaken by the FDIC in 1991, which requires banks to take into account interest rate risk.

For aggregate illiquidity there is no noticeable pattern in the coefficient estimates except that they

are negative and insignificant once the bootstrap is used to calculate p-values. The dichotomy of the as-

ymptotic and bootstrap results for aggregate illiquidity is significant. The recent literature on illiquidity

is based on asymptotic test-statistics and the conclusions have been in favor of an illiquidity premium.

The conclusion of this paper would be the same if I did not take into account the poor finite sample

behavior of method of moments estimators. We should therefore take greater care in interpreting recent

empirical work on illiquidity.

For bank-specific illiquidity, there are marked differences in table 11 which includes the long-term

rate and 12, which uses the intermediate-term rate. The coefficients are increasing fromr5 to r1 but

not monotonic. Bank-specific illiquidity risk sensitivity is negative and significant using the long-

term interest rate and negative but insignificant with the intermediate-term rate. For the case with the

intermediate-term interest rate there are differences between the asymptotic p-values and bootstrapped

p-values. The latter accepts the null hypothesis that banks-specific illiquidity is insignificant.

Figure 11 displays the bootstrap density of the overidentification test statistic versus the asymptotic

density with twenty degrees of freedom corresponding to table 11. The asymptotic critical values would

16

tend to over-reject a null hypothesis relative to the bootstrap critical values. Given the test-statistic, the

model is not rejected for either the asymptotic or the bootstrap approximations.

I report GMM estimates of the portfolios sorted by size in tables 13 and 14. The coefficients mea-

suring the sensitivity of U.S. bank stock returns to interest rate risk are all insignificant. The coefficients

on aggregate illiquidity have no discernable pattern if one uses the correct bootstrap p-values. Using

the incorrect asymptotic p-values would lead us to think the coefficients are significant and increasing

in size. The bank-specific coefficients are negative, increase in size, and significant in table 13, which

includes the long-term interest rate. That is, the effect of an increase in unexpected illiquidity decreases

the stock return and this effect is more pronounced for smaller stocks. This corresponds to a flight to

size effect where investors view larger banks as more diversified and less exposed to illiquidity in the

commercial paper market. The coefficients are insignificant when the intermediate-term government

bond is used for interest rate risk.

6 Conclusion

This paper analyzes the transformation of U.S. banking from lenders to service providers in the

past twenty years. In particular, I examine illiquidity risk in the commercial paper market. Banks

have increasingly provided loan commitments to firms floating commercial paper for a fee. This fee

has increased profits but also risk. I find that investors require banks in general to reward them for

illiquidity risk in the commercial paper market and smaller banks to reward them for this risk more

than larger banks.

The lack of evidence supporting the role of aggregate illiquidity in US banking leads to questions

about the role of aggregate illiquidity in the broader market. Once test statistics are bootstrapped

illiquidity is found to be unimportant for U.S. bank stock returns. Further research on illiquidity should

take this into account.

17

Table 11: Estimation: Portfolios sorted by banks-specific illiquidity 1984:1-2003:12

r j,t = β j0 +β jM Mt +β jI I1t +βA

jL ILAt +βB

jL ILBt +η j,t

r1 r2 r3 r4 r5

α0 0.0024 0.0040 0.0043 0.0018 0.0010[0.3085] [0.1048] [0.0275] [0.2349] [0.3582](0.5786) (0.3344) (0.1605) (0.4916) (0.6923)

βM 0.9344 0.9112 0.8632 0.8739 0.8169[0.0000] [0.0000] [0.0000] [0.0000] [0.0000](0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

β1I -0.1908 -0.0083 0.0061 -0.0209 0.0210

[0.1117] [0.4678] [0.4691] [0.3956] [0.3966](0.3645) (0.7625) (0.7291) (0.7592) (0.6722)

βAL -0.0480 -0.0482 -0.0351 -0.0419 -0.0256

[0.0335] [0.0220] [0.0379] [0.0201] [0.0961](0.1641) (0.1438) (0.1706) (0.1304) (0.3344)

βBL -34.083 -21.245 -14.123 -13.850 -14.760

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000](0.0167) (0.0268) (0.0301) (0.0301) (0.0100)

J-test 19.719[0.4756](0.7458)

Note: portfolios are ranked by sensitivity to illiquidity risk from most sensitive (r1) to least sensi-tive (r5). M is excess market risk,ILA is the unexpected aggregate illiquidity.ILB is the unexpectedbank-specific illiquidity risk;I1 is the unexpected interest risk proxied by the long-term govern-ment bond. The set of instruments isZt = {Mt , I1

t , IL it ,Mt−1, I1

t−1, ILit−1} for i = A,B. Asymptotic

p-values given in brackets and Bootstrapped p-values are in parentheses.

18

Table 12: Estimation: Portfolios sorted by banks-specific illiquidity 1984:1-2003:12

r j,t = β j0 +β jM Mt +β jI I2t +βA

jL ILAt +βB

jL ILBt +η j,t

r1 r2 r3 r4 r5

α0 0.0010 0.0038 0.0058 0.0031 0.0001[0.4112] [0.0683] [0.0009] [0.0759] [0.4830](0.8294) (0.4047) (0.0836) (0.3612) (0.8161)

βM 0.9142 0.9517 0.8979 0.8796 0.8782[0.0000] [0.0000] [0.0000] [0.0000] [0.0000](0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

β2I -0.5178 -0.2477 -0.1825 -0.2930 -0.1174

[0.0292] [0.0627] [0.1078] [0.0137] [0.2213](0.3545) (0.5084) (0.4749) (0.2676) (0.5017)

βAL -0.0514 -0.0555 -0.0280 -0.0233 -0.0246

[0.0374] [0.0018] [0.0249] [0.0794] [0.0924](0.3177) (0.1706) (0.2876) (0.5452) (0.5953)

βBL -33.3195 -17.0818 -7.7057 -8.5953 -16.7228

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000](0.0602) (0.1237) (0.0535) (0.0568) (0.0936)

J-test 22.557[0.3110](0.9164)

Note: portfolios are ranked by sensitivity to illiquidity risk from most sensitive (r1) to least sensitive(r5). M is excess market risk,ILA is the unexpected aggregate illiquidity.ILB is the unexpectedbank-specific illiquidity risk;I2 is the unexpected interest risk proxied by the intermediate-termgovernment bond. The set of instruments isZt = {Mt , I2

t , IL it ,Mt−1, I2

t−1, ILit−1} for i = A,B. As-

ymptotic p-values given in brackets and Bootstrapped p-values are in parentheses.

19

Table 13: Estimation: Portfolios sorted by size 1984:1-2003:12

r j,t = β j0 +β jM Mt +β jI I1t +βA

jL ILAt +βB

jL ILBt +η j,t

r1 r2 r3 r4 r5

α0 -0.0090 0.0017 0.0053 0.0034 0.0045[0.0230] [0.2784] [0.0166] [0.0553] [0.0113](0.1806) (0.5920) (0.1003) (0.2207) (0.0635)

βM 0.7083 0.7210 0.8958 1.0205 1.1929[0.0000] [0.0000] [0.0000] [0.0000] [0.0000](0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

β1I -0.1272 -0.1328 0.0174 -0.0527 0.1664

[0.2156] [0.1152] [0.4052] [0.2128] [0.0067](0.4147) (0.2876) (0.6722) (0.3478) (0.0301)

βAL -0.0531 -0.0827 -0.0561 -0.0416 -0.0062

[0.0395] [0.0009] [0.0092] [0.0175] [0.3563](0.2040) (0.0201) (0.0970) (0.1070) (0.5418)

βBL -38.5956 -22.1129 -15.3433 -8.8144 -2.2430

[0.0000] [0.0000] [0.0000] [0.0000] [0.1045](0.0100) (0.0067) (0.0134) (0.0067) (0.2475)

J-test 22.470[0.3156](0.3980)

Note: portfolios are ranked by market capitalization from smallest (r1) to largest (r5). M is excessmarket risk,ILA is the unexpected aggregate illiquidity.ILB is the unexpected bank-specific illiq-uidity risk; I1 is the unexpected interest risk proxied by the long-term government bond. The setof instruments isZt = {Mt , I1

t , IL it ,Mt−1, I1

t−1, ILit−1} for i = A,B. Asymptotic p-values given in

brackets and Bootstrapped p-values are in parentheses.

20

Table 14: Estimation: Portfolios sorted by size 1984:1-2003:12

r j,t = β j0 +β jM Mt +β jI I2t +βA

jL ILAt +βB

jL ILBt +η j,t

r1 r2 r3 r4 r5

α0 -0.0076 0.0033 0.0061 0.0047 0.0035[0.0746] [0.1684] [0.0075] [0.0149] [0.0347](0.3378) (0.5084) (0.1003) (0.1472) (0.1438)

βM 0.8075 0.7817 0.9633 1.0442 1.3336[0.0000] [0.0000] [0.0000] [0.0000] [0.0000](0.0013) (0.0000) (0.0000) (0.0000) (0.0000)

β2I -0.3317 -0.0782 -0.3015 -0.0088 -0.0890

[0.1901] [0.3649] [0.0333] [0.4733] [0.2313](0.4348) (0.6990) (0.1873) (0.6020) (0.4849)

βAL -0.0753 -0.0894 -0.0634 -0.0452 0.0012

[0.0113] [0.0003] [0.0040] [0.0038] [0.4701](0.2676) (0.0468) (0.1003) (0.0535) (0.5385)

βBL -49.3258 -28.9500 -16.9731 -8.5369 -3.6942

[0.0000] [0.0000] [0.0000] [0.0000] [0.0188](0.2274) (0.2977) (0.1405) (0.0769) (0.1839)

J-test 18.276[0.5692](0.9833)

Note: portfolios are ranked by market capitalization from smallest (r1) to largest (r5). M is excessmarket risk,ILA is the unexpected aggregate illiquidity.ILB is the unexpected bank-specific illiq-uidity risk; I2 is the unexpected interest risk proxied by the intermediate-term government bond.The set of instruments isZt = {Mt , I2

t , IL it ,Mt−1, I2

t−1, ILit−1} for i = A,B. Asymptotic p-values

given in brackets and Bootstrapped p-values are in parentheses.

21

Table 15: Data Description

Commercial Paper Unsecured short-term loan to high-credit firms,finances account receivables and inventories

Loan commitment Unused commitments to make (purchase) loans/extend credit

Unused loan commitments Unused portion of loan commitments,a fee has been paid and the bank is legal committed.

Net Interest Income Earnings from balance sheet assets net of interest costs

Standard NII Income from Fiduciary activitiesService charges on deposit accounts in domestic officesTrading gains (losses)Fees from foreign exchange transactions gains (losses)Other foreign transactionsOther gains (losses) and fees from trading assets/liabilities

Additional NII Investment banking, advisory, brokerage, and underwritingVenture revenueNet servicing feesNet securitization incomeNet gains (losses) on sales of loansNet gains (losses) on sales of real estate ownedNet gains (losses) on sales of other assets (excluding securities )Other Non-Interest Income

Fiduciary activities Income from services rendered by the institution’s trustdepartment or by any of its consolidated subsidiaries actingin any fiduciary capacity

Trading gains (losses) Net gains and losses from trading cash instruments and OBSderivative contracts that have been recognized during theaccounting period.

Note: Non-Interest Income = Standard Non-Interest Income + Additional Non-Interest Income. Definitions provided by theFDIC. The website is: www2.fdic.gov/sdi

22

Figure 1: Share of Non-Interest Income in Net Operating Revenue

20.00

25.00

30.00

35.00

40.00

45.00

3/1/1984 3/1/1986 3/1/1988 3/1/1990 3/1/1992 3/1/1994 3/1/1996 3/1/1998 3/1/2000 3/1/2002 3/1/2004

Per

cen

t

Figure 2: Unused Loan Commitments as a fraction of Unused Loan Commitments plus Loans

0

10

20

30

40

50

60

Mar-84 Mar-86 Mar-88 Mar-90 Mar-92 Mar-94 Mar-96 Mar-98 Mar-00 Mar-02

23

Figure 3: Unused Loan Commitments

000.0E+0

1.0E+9

2.0E+9

3.0E+9

4.0E+9

5.0E+9

6.0E+9

7.0E+9

Mar

-84

Mar

-85

Mar

-86

Mar

-87

Mar

-88

Mar

-89

Mar

-90

Mar

-91

Mar

-92

Mar

-93

Mar

-94

Mar

-95

Mar

-96

Mar

-97

Mar

-98

Mar

-99

Mar

-00

Mar

-01

Mar

-02

Mar

-03

$

Figure 4: Loans

000.0E+0

1.0E+9

2.0E+9

3.0E+9

4.0E+9

5.0E+9

6.0E+9

Mar-84 Mar-86 Mar-88 Mar-90 Mar-92 Mar-94 Mar-96 Mar-98 Mar-00 Mar-02

$

24

Figure 5: Commercial Paper of Nonfinancial Companies: Market Size in Billions of $

0

50

100

150

200

250

300

350

400

Jan-

84

Jan-

85

Jan-

86

Jan-

87

Jan-

88

Jan-

89

Jan-

90

Jan-

91

Jan-

92

Jan-

93

Jan-

94

Jan-

95

Jan-

96

Jan-

97

Jan-

98

Jan-

99

Jan-

00

Jan-

01

Jan-

02

Jan-

03

Bill

ion

$

Figure 6: Portfolio Rankings based on Sorting by Illiquidity

1982 1985 1987 1990 1992 1995 1997 2000 20020

1

2

3

4

5

Por

tfolio

Ran

k

JP Morgan

25

Figure 7: Unexpected Long-term Interest Rate Risk

1982 1985 1987 1990 1992 1995 1997 2000 2002 2005−0.2

−0.15

−0.1

−0.05

0

0.05

0.1

0.15

Time

Une

xpec

ted

Cha

nges

Figure 8: Unexpected Intermediate-term Interest Rate Risk

1982 1985 1987 1990 1992 1995 1997 2000 2002 2005−0.04

−0.03

−0.02

−0.01

0

0.01

0.02

0.03

0.04

Time

Une

xpec

ted

Cha

nges

26

Figure 9: Unexpected Bank-Specific Illiquidity

1982 1985 1987 1990 1992 1995 1997 2000 2002 2005−6

−4

−2

0

2

4

6

8

10x 10

−3

Time

Une

xpec

ted

Cha

nges

Figure 10: Unexpected Aggregate Illiquidity

1982 1985 1987 1990 1992 1995 1997 2000 2002 2005−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

Time

Une

xpec

ted

Cha

nges

27

Figure 11: Density Estimate of the Overidentifying Restrictions Test

−20 −10 0 10 20 30 40 50 60 70 80−0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

J−test

Fre

quen

cy

bootstrapasymptotic

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