The influence of bank capital on the lending behavior

The influence of bank capital on the lending behavior

Credit channels in the U.S.

K. (Karim) Harakeh

UNIVERSITY OF GRONINGEN

Faculty of Economics and Business

MSc Business Administration, Specialization Finance

Supervisor: prof. dr. K.F. (Kasper) Roszbach June 2013, Groningen

Abstract

I collect quarterly data from the Call Reports, which all insured banks are required to submit to the Federal Reserve each quarter. Using these reports I analyze the effects of monetary policy changes and macroeconomic factors on the lending behavior of banks. I find evidence of a bank lending channel and a bank capital channel of monetary policy in the U.S. from Q4 2002 to Q4 2012. My research also provides empirical evidence that bank capital plays an important role in the context of the credit channels. Furthermore, banks seem to be exposed to GDP growth where it appears that well-capitalized banks are better able to insulate the effects of GDP on their lending compared to low-capitalized banks.

JEL classification: E44; E52; G21

Keywords: monetary policy; monetary transmission mechanisms; bank lending; bank capital

1 _{Student number: s2181681}

Table of Contents

1. Introduction ... 2

2. Literature overview ... 4

2.1 General overview ... 4

2.2 The role of bank capital ... 8

I. Introduction

Banks as “financial intermediaries” have a pivotal role in the economy as they provide lending to firms and households. In fact, their role as providers of credit is particularly crucial in economic downturns and financial distress. However, in reality during recessions banks seem to do the opposite of what is desired namely issuing fewer loans. This phenomenon in bank lending can be caused by either the reluctance of banks to lend money as they become more risk-averse, or because of the negative impact that a recession has on the creditworthiness of borrowers which makes potential borrowers ineligible for bank loans.

There are several factors that could influence the lending behavior of banks. One factor, as is already mentioned in the first paragraph, has to do with macroeconomic factors such as GDP growth. Monetary policy changes are another factor, which may also play a very important role, since monetary policy aims at influencing the availability and cost of money and credit in order to stimulate lending in times of recession and discourage lending in prosperous economical periods (i.e. to avoid overheating of the economy). This means that the Federal Reserve (Fed) would intervene by conducting monetary policies with the intention to keep the balance of credit intact and foster stable economic growth. Furthermore, the Fed has several monetary tools at its disposal with the purpose to set a target for the key interest rate, i.e. the federal funds rate. At the moment of writing, the Fed is maintaining the federal funds rate at 0% since the fourth quarter of 2009 as a reaction to the financial crisis that hit the U.S. economy. The magnitude of the crisis was unprecedented causing Lehman Brothers, a giant in the financial sector, to collapse which was the largest bankruptcy filing in the U.S. history. The federal funds rate has never been so low for such a long time period, which is a consequence of the prolonged financial crisis. This can be interpreted as an expansionary policy of the Fed with the aim to increase bank lending since this is one of the driving factors behind economic growth.

raise non-deposit sources of funds and therefore banks‟ ability to limit the effect of a drop in deposits on lending. Since then, much research has been done on the effect of monetary policy on the lending behavior of banks taking bank capital into account.

In this paper, I will mainly study the effect of monetary transmission mechanisms on bank lending. Studies of the “traditional” monetary transmission mechanism initially focused on how a change in the reserves alters interest rates and deposits which would lead to a persistent change in the overall spending. My paper will focus similar to the work of Gambacorta and Mistrulli (2004) on two transmission channels which are the bank lending channel and the bank capital channel. In the analysis of the bank lending channel, I will use the excess risk-based capital of banks to investigate whether changes in the monetary policy have affected bank lending due to the different capitalization ratios of banks. The thought underlying this channel is that banks with higher capital ratios will find it easier to raise new funds, in a situation where deposits decrease or other forms of reduction occur on the liability side of the bank‟s balance sheet, compared to low-capitalized banks. Next, I will discuss the bank capital channel. The notion underlying this theory is that monetary policy changes could affect the bank‟s capital through the maturity transformation (exposure of interest rate risk) of the bank. The role of capital in the bank capital channel is as follows: due to a monetary tightening, banks will see their profits as well as capital accumulation decrease, and if issuing new shares is too costly, banks would adjust their lending in order to meet the capital regulation requirements. Thus, the channels to be analyzed assume that capital plays an important role in the lending behavior of banks. The research question of this thesis, which is an extension of the work of Gambacorta and Mistrulli (2004) by including a larger and different dataset of banks, is formulated as follows: “How has bank capital influenced the bank lending in the U.S. as a

consequence of monetary policy and macroeconomic changes between Q4 2002 and Q4 2012?” More

specifically, the main purpose of this study is to empirically investigate this question. My study differs principally from the work of Gambacorta and Mistrulli (2004) in two ways: first, they include in their analysis in addition to Italian banks a sample of Italian credit cooperatives and second, they use the Generalized Method of Moments (GMM) estimator in their analysis whereas I use the Least Square Dummy Variables (LSDV) model.

II. Literature overview

2.1 General overview

In the existing literature, a lot of research has been reported which examines the monetary transmission channels that explain how monetary policy decisions affect the real economy. In theory, there are at least two different ways in which the level of bank equity can amplify the impact of monetary changes on bank lending. Firstly, bank lending can be affected through the conventional bank lending channel. Secondly, it can be affected through a more direct mechanism denoted as the bank capital channel. Interestingly, both theories rely on imperfect credit markets. This is in contrast to the proposition of the Modigliani-Miller theorem for banks which we will discuss later in this section.

Previous empirical literature studying the effect of bank capital on lending is predominantly focused on the U.S. banking system (Furfine, 2000; Hancock et al., 1995; Kishan and Opiela, 2000; Van den Heuvel, 2001b). What these articles have in common is that they find evidence that bank capital is of great importance in influencing the lending behavior of commercial banks. In addition, an early study of Friedman and Schwartz (1963) has found evidence that monetary policy actions are followed by movements in real economic output that may last for two years or more, implying that the lending behavior of banks also gets affected by these actions. Similarly, Thakor (1996), who used an asymmetric-information model of bank lending and simultaneously maintained the assumption of costly external funds, has shown that monetary policy impacts bank lending. Furthermore, Bernanke and Blinder (1992) proved in a very prominent paper that interest-rate shocks affect the size and composition of the bank‟s portfolio (securities and loans) with a lag in time starting half a year later and lasting up to two years. According to the Modigliani-Miller theorem, shocks to the liability side of a bank‟s balance sheet should not make a bank reluctant to provide loans at any interest rate. This is consistent with the findings of Romer and Romer (1990) in which they claim that banks can always find alternative sources of finance other than deposits, meaning non-deposit (uninsured) sources of funds.2 Thus, even though the Fed is

2

following a contractionary monetary policy, banks will still be able to raise funds, for example by issuing new Certificates of Deposit (CD) to compensate for the shortfall in deposits. The bottom line of the work of Romer and Romer (1990) is that bank‟s loan supply is completely unaffected by monetary policy. The bank lending channel hypothesis suggests that due to imperfect markets, which prevent banks from raising uninsured funds when exposed to contractionary monetary policy shocks, constrained banks will find themselves in a position forcing them to adjust their lending. This is consistent with the findings of Kashyap and Stein (1995) in which they claim that monetary policy will work in part through a lending channel: when the Fed drains deposits from the system, financial intermediaries cannot make up the funding shortfall without friction by raising non-deposit external finance, and this will eventually alter the lending behavior of the banks. However, the bank lending channel indicates that well-capitalized banks are in a better position to shield their lending from monetary policy shocks as they have easier access to raising uninsured funds.

Moreover, due to the imperfect markets for bank debt and non-reservable liabilities, especially in situations of financial distress or a negative outlook of the economy, a “lemon‟s premium” has to be paid to investors. In this case, bank capital will be considered as an important item because lower capital levels impact the creditworthiness of the bank and this in turn will lead to higher prices for uninsured liabilities (Flannery and Sorescu, 1996). Therefore, the cost of wholesale funding (i.e. bonds or CDs) would be higher for undercapitalized banks if they are perceived as being more risky by the financial market. Another important factor that might weaken the bank lending channel is the existence of risk-based capital requirements. Since a bank-capital‟s ability to absorb shocks is a measure of bank‟s health, and therefore, is an indicator of a bank‟s ability to raise alternative external funding during periods of monetary tightening. When a contractionary monetary policy takes place the lending of a capital-unconstrained bank may either remain unaffected or even rise. In fact, Kishan and Opiela (2000) found that the lending behavior of small undercapitalized U.S. banks (total assets <300M USD) is the most responsive to monetary policy.

bank may optimally pass profitable lending opportunities at a certain moment in order to reduce the risk of future capital inadequacy. However, if banks were always able to raise new equity, no bank would ever allow profitable lending opportunities to pass by and the capital requirements would be irrelevant except insofar as equity has a higher required rate of return. Myers and Majluf (1984) provide theoretical evidence to support the assumption that issuing new equity can be quite costly under certain circumstances.

Banks confronted with binding capital constraints as a result of high charge-offs and low or no earnings have only two options to increase their capital/asset ratios. The first method is by raising new capital (e.g. issuing new common stock), and the second method is by shrinking mainly their assets (Peek and Rosengren 1995). Furthermore, Hancock and Wilcox (1992) claim that losses of bank capital can cause banks to shrink their assets and liabilities to restore target capital/asset ratios in response to regulatory pressures, financial market pressures, or the tastes and preferences of bank management. In agreement with Hancock and Wilcox (1992), Peek and Rosengren (1995) found evidence that there is a strong positive relation between shocks to a bank's capital and the growth rate of its deposits. Accordingly, the bank capital channel will be tested in this study by looking at the effect of the change in the bank‟s profit on its capital.

Technically speaking, banks consider loans and securities as interchangeable assets. Therefore, when the demand for loans strengthens, it is proposed that banks sell their securities in order to issue more loans. This is in contrast with a weaker demand for loans, because in such scenario banks will tend to hold more securities. Banks can use investments in securities to manage the interest-rate risk inherent in their core business (Beatty and Bettinghaus, 1997). Furthermore, according to Kashyap and Stein (1995) the main difference between loans and securities is that securities can be liquidated without costs. Due to this better “liquidity” in equilibrium, banks would prefer to hold securities even when these offer an inferior return compared to loans.

Another aspect, which plays an important role in bank performance, is bank governance. Looking at a more recent study, Beltratti and Stulz (2012) found strong evidence that banks, which relied more on deposits for their financing, and large banks, which had less leverage in 2006, performed better during the crisis. In addition, some literature emphasizes that failings in bank governance played a major role in the performance of banks (Diamond and Rajan, 2009, and Bebchuk, Cohen and Spamann, 2010). The idea is that banks with poor governance were engaged in taking excessive risk which increased their market exposure and resulted in larger losses during the crisis. Furthermore, Cornett et al. (2011) found that U.S. banks with more exposure to liquidity risk experienced less loan growth during the crisis. Beltratti and Stulz (2012) also found that banks with large stock returns in 2006 were the banks the stocks of which suffered the largest losses during the crisis. This article also provides evidence that banks from countries with stricter regulations in 2006 fared better during the crisis. This is understandable because banks with more restrictions on their banking activities were able to achieve higher returns because they did not have the opportunity to diversify into activities that unexpectedly performed poorly during the crisis.

Besides monetary policy shocks, bank lending is also exposed to macroeconomic influences. A reason for this could be the high pro-cyclicality of credit. In fact, during recessions the amount of newly issued loans reduces dramatically. This could be explained either by the supply side (bank lending channel) or by the weakening of the demand for loans (a demand shift), or by both. Apart from that, the lending behavior of banks could react differently with respect to the business cycle.

that capital is negatively correlated with credit risk. In other words, the loan supply of well-capitalized banks is less dependent on the state of the economy compared to low-capitalized banks. This is consistent with the findings of Flannery (1989) and Gennotte and Pyle (1991), who assert that better capitalized banks are more risk-averse as they select their clients (w.r.t. loans) more carefully with the emphasis on minimizing the probability of defaulting. As a result, during an economic recession, well-capitalized banks would have relatively lower charge-offs and their capital level would change less due to the lower unexpected losses in comparison with other banks.

2.2 The role of bank capital

Bank capital is important as it serves as the cushion to absorb any unexpected losses incurred by the bank. It is basically the difference between the value of bank‟s assets and liabilities and it is also known as the net worth of the bank. However, it also serves as the source for paying dividends to the shareholders in times of high return. Thus, since the position of banks in any economy is pivotal, as suppliers of credit and financial liquidity, so is bank capital. Therefore, banks are greatly regulated by the Fed, and their capital is constantly subject to international regulatory rules directed by the Bank for International Settlements (BIS), situated in Basel, Switzerland.

Total capital is comprised of retained earnings, reserves, equity capital, preference share capital, hybrid capital instruments and subordinated debt. Total capital can be subdivided by Tier 1 and Tier 2 capital. The former includes the first three items mentioned and the latter the last three items. Difference between Tier 1 and Tier 2 capital ratios is that the former is considered to be the core (highest quality) capital as a percentage of the bank‟s total assets adjusted for risk (risk-weighted assets) using global banking guidelines. Tier 2 is considered to be the lower quality capital as it cannot serve to absorb losses and in contrast to Tier 1 it is repayable and has a shorter term horizon than equity capital. As a study of Beltratti and Stulz (2009) showed, large banks with (relatively) higher Tier 1 capital and more deposit financing at the end of 2006 had significantly higher returns during the financial crisis 2007-2009.

2.3 Monetary policy

The Fed is the Central Bank of the U.S. which determines the U.S. monetary policy. The Fed is enabled by law to undertake actions to influence the availability and cost of money and credit in order to maintain sound economic policies to foster growth, sustainable employment and retain stable inflation levels. In order to achieve its goals the Fed sets a target for a key interest rate, the federal funds rate, with the help of the following three monetary policy tools: the reserve requirements (RR), the discount rate and the open market operations. By employing these tools jointly or individually, the Fed is able to set and control the supply of money and credit.

Monetary policy can be subdivided into two contradictory policies also known as the expansionary and the contractionary policies. While the former policy aims to increase the supply of money in the economy, the latter seeks the reverse. Both strategies work through the monetary transmission mechanism. This means that when the Fed decides to change the RR, the discount rate and/or buy/sell bonds (or treasuries) on the open market, it will impact the real economy, since it will affect the policies of the financial intermediaries (i.e. the commercial banks) on their lending behavior. For example, in a recession (or recessionary gap) it will be very likely that the Fed will foster economic growth by increasing the money supply. Increasing the money supply leads to lower interest rates which in turn increase the amount of investments in the economy due to more favorable borrowing conditions. This in turn will shift the aggregate demand curve upwards in the macroeconomic setting. Important to mention here is that expansionary monetary policy goes hand in hand with an increase in price levels, i.e. it has an inflationary trend.

A contractionary monetary policy can take place when the Fed believes that the economy is overheating. The tools they can apply to slow down the economy are increasing the RR, increasing the discount rate and/or sell bonds (or treasuries) on the open market. This is clearly exactly the opposite of the case of an expansionary policy. In addition, Cook and Hahn (1989a) confirm this effect through finding evidence that when the Fed conducts contractionary open-market operations (i.e. selling bonds), interest rates for fixed-income securities of all maturities typically rise, and thus make it less enticing to borrow money for investments.

III. Hypotheses

The above observations made in the literature motivate me to propose the following hypotheses, which I will test in this thesis. The first hypothesis has to do with testing the bank lending channel.

H1: The impact of monetary policy actions differs amongst banks with a different degree of capitalization.

The second hypothesis considers the bank capital channel. The underlying thought is that a reduction in the bank‟s capital accumulation might have a negative impact on bank lending. This thesis hypothesizes that a change in profitability alters the capital accumulation and hence the bank lending.

H2: Bank lending is influenced by a change in profitability giving rise to the bank capital channel.

The last hypothesis is related to the difference in risk behavior of banks. The propagated idea is that well-capitalized banks are more risk-averse as they can select their clients, thus minimizing their risks. This

hypothesis in which banks with higher capital levels are expected to be more risk-averse can also be substantiated by considering capital as a cushion against certain economic scenario‟s (Dewatripont and Tirole, 1994; Repullo, 2000; Van den Heuvel, 2001a). On the other hand, bank lending of low-capitalized banks are more dependent on the state of the economy as their bank lending tend to be more exposed to risks.

IV. Methodology

4.1 Endogeneity problem

Endogeneity is a common problem in research that cannot be neglected in estimating (financial) models. In fact, it threatens the validity of models that make causal claims regarding the relationship between the independent variable(s) and the dependent variable.

Having a strong correlation and significant relationship between the independent and dependent variables does not necessarily mean that it reflects a true relationship. The problem is that the independent variables may not be independent after all and that there exists a two-way relationship of dependence between the variables. This indicates a situation of an omitted course which is not observable. In order to tackle this endogeneity problem, all the regressors related to the bank specific characteristics will refer to those of the (t – 1) quarter to avoid an endogeneity bias.

Taking the above into consideration, I performed a Hausman (endogeneity) test in the model described in the following subsection in order to check whether the explanatory variables in the regression are actually exogenous. This test allows me to determine which is applicable: the random effects or the fixed effects. By conducting the Hausman test, I obtained a p-value of 1 indicating that both random and fixed effects are applicable.

Endogeneity can have different causes, but it especially occurs by omitting variables and introducing fixed effects into the model can greatly reduce the probability that a relationship is driven by an omitted variable.

4.2 Methodological approach

This study employs the LSDV regression with cross-sectional fixed effects to perform empirical research on the impact of monetary policy effects and macroeconomic changes on the lending behavior of banks. The model can also be described as a dynamic panel-data model due to the use of lagged-dependent variables as regressors. In literature, a popular model to estimate dynamic panel data is the GMM estimator suggested by Arrelano and Bond (1991), which is designed for panel data consisting of a large number of banks (N) and a small number of quarters (T). The latter model allows for endogeneity among the independent variables by introducing instrumental variables. However, this model is very complicated and can easily generate invalid outcomes.

the sample contains 41 time periods, which is relatively large, the impact on the bank‟s apparent fixed effect would decrease together with the endogeneity problem. In addition, Roodman (2006) confirmed that a sample with a large (T) will remove the dynamic panel bias, and a more straightforward fixed effects estimator would be appropriate.3 Moreover, data were transformed into first-differences in order to make the estimation more consistent and to function as a potential remedy for auto-correlated residuals.4 To test whether auto-correlation in my sample is under control, I estimated the LSDV model in two different ways with and without robust standard errors and noticed that there were no big differences in the computed standard errors. Furthermore, coefficient standard errors are adjusted by employing panel-corrected standard errors in order to control for heteroskedasticity.

To test the hypotheses of the bank capital channel, bank lending channel and the difference in risk aversion of banks, I estimate the following model, due to Gambacorta and Mastrulli (2004), as described below: ∑ ∑ ∑ ∑ ∑( ) ∑( ) (1)

Lit Loans of bank i in quarter t

MPt Monetary policy indicator

πt Inflation rate

γt Real GDP

Xit Measure of excess capital

NIIit Net Interest income divided by average earning assets

φ Set of control variables: (lnTA and Liq)

As can be noticed from the model, the variables, (Lit) and (γt) are transformed in natural logs. The use of

the excess capital (X) - i.e. regulatory capital minus required capital - variable which is normalized around its mean has two important implications in this model. First, the sum of the two interaction terms in model (1) are zero for the average banks as the mean of the excess capital variable is also equal to zero.

3

The fixed effects control for unobserved heterogeneity among banks.

4

Second, the meaning of the coefficients βj and δj can be easily interpreted as the average effect of monetary policy and GDP, respectively. Furthermore, all the bank-specific variables (X, NII, lnTA and

Liq) in my estimation are stated in lags (t-1) to prevent endogeneity. In fact, lagged variables are by

nature exogenous, since an occurrence in the future cannot influence the past. Additionally, the macro-economic factors in my model are assumed to be strictly exogenous.

This linear model estimates long-run coefficients, to test the effect of monetary policy (∑ )5,

inflation(∑ ) and GDP changes (∑ ).6Since these variables are assumed to be strictly

exogenous, as a two-way relationship is excluded, also t=0 on top of the four lags will be added in order to calculate the long-run coefficients. Additionally, the equation includes the first-difference of Net Interest Income (NII) of the previous quarter in order to investigate the presence of the bank capital channel (see Appendix A). Following Gambacorta and Mistrulli (2004), I also considered the two interaction terms being the product of excess capital with monetary policy and excess capital with GDP changes, to examine the bank lending channel and the difference in risk behavior, respectively.

The presence of multicollinearity has been checked among the independent variables and no abnormal correlations have been found.7 The stationarity of the dependent and independent variables has also been taken into consideration by performing the unit root test suggested by Levin and Lin (1992). Fortunately, all the variables are stationary and do not to follow a unit root process (p-value < 0.01) as this could lead to spurious regressions.

5 _{The long-run elasticity of lending with respect to monetary policy, GDP shocks and inflation is calculated by}

∑ ( ∑ )

6

Dummy variables are included to examine the degree to which seasonality is present and to remove any possible serial correlation in which the residual structure is cyclical in shape.

7

V. Data description

Bank specific data used in the present estimation have been retrieved from the Reports of Condition and Income, also known as the “Call Reports” 8_{, collected by the FDIC. All insured “commercial” banks and} savings associations in the U.S. are required to submit accurate financial data regarding their current financial position and their operational results on a quarterly basis. This dataset of financial institutions is of importance for this thesis as it focuses on activities based on deposits and loans. The data collected for this study are quarterly and cover a time span from the fourth quarter of 2002 to the fourth quarter of 2012. This means that the dataset includes 41 time periods (T=41).

The sample consists of 2,875 FDIC insured banks (N=2,875). The composition of the sample has been determined by the imposition of a few selection criteria in order to obtain appropriate data for the estimation. First, banks were only included if a significant proportion of their portfolio consisted of loans (i.e. banks should have had at least 35% of their portfolio consisting of loans during the whole period). Second, Mergers and Acquisitions (M&A) were accounted for by removing all banks that experienced an increase of 15% in total assets in a consecutive period. In addition, banks that showed an extraordinary decrease or increase in the (12 month) loan growth rate are eliminated from the sample also to remove any possible bias due to M&As.9 Naturally, a more professional way would be to filter out the data regarding M&A activities by using M&A files. However, this method is beyond the scope of this study because of lack of access to the data and therefore the assumption is adopted that if total assets increase by 15% compared to the preceding period it would pertain to M&A activities. This assumption may bias the results in case medium-sized banks indeed realize more than 15% growth without M&A, but I expect the numbers of such banks that experience a 15% growth in total assets to be small. Third, the sample only includes banks where all information (such as bank‟s loans, excess capital, liquidity, etc.) is available in the Call Reports with respect to the input variables required to perform the regression analyses as in equation (1). Fourth, only the banks that are completely covered in the Call Reports in the period starting from Q4 2002 to Q4 2012 are included, thus for 41 periods. This eliminates banks from the sample that went bankrupt or stopped their banking activities during the period in question. Due to the latter selection criteria each bank now has the same number of time observations (also known as “balanced panel data”) yielding a total of 117,875 observations. Finally, a small number of illogical extreme outliers were removed that might affect the results of the analyses negatively.

8 _{The Call Reports have been downloaded from the official website of the FDIC} 9

The dependent variable used in the estimations is the “total gross loans” of banks and will be denoted as (Lit). Furthermore, an independent variable representing the excess capital will be included. However, this variable will be adjusted in a way to test for the existence of asymmetric effects due to bank capital by the following normalization adjustment as proposed by Gambacorta and Mistrulli (2004):

(∑

∑ ₎

(2)

where (EC) and (RWA) stand for excess capital (Regulatory capital – capital requirements) and risk-weighted assets, respectively.10 Furthermore, i and t are indices running over bank number and quarter number, respectively, and T is the total number of quarters. This adjustment leads to a normalization of the indicator around its average across all banks. This has an important implication for the tests of this study with respect to the interaction terms used in the regression.

In order to test for the bank capital channel the Net Interest Income divided by total average earning assets (NII) has been introduced. The notion behind the introduction of this variable is that an increase or decrease in the NII of a bank, thus either accumulating or dissipating bank capital, can lead to a policy change of the bank with respect to its bank lending behavior11.

A set of bank specific control variables will also be used in the regression analyses and consist of a liquidity indicator and the natural log of total assets.12 This paper uses the ratio of short-term investments to total assets as a measure of the liquidity variable (Liqit). Short-term investment is defined as the sum of interest-bearing bank balances, federal-funds sold securities and debt securities with a remaining maturity of < 1 year. Furthermore, the liquidity indicator is normalized around its mean over the whole sample period similar to the computation of (Xit). The total asset variable is denoted as (TAit) and has been normalized with respect to the mean for each single period. This measure has been adopted in order to eliminate trends in size.

(∑ ∑ ) (3)

10

The risk categories to determine the risk-weighted assets are still based on the Basel 1 accord because the U.S. has never implemented the Basel 2 accord. The Office of the Comptroller of the Currency (OCC) requires banks to have a minimum total risk-based capital ratio of 8%. However, there are some restrictions to this ratio such as the minimum capital of 8% should consist at least for the half of Tier 1 capital. However, for simplicity this study assumes that 8% of capital is the required without other requirements. Therefore, 8% will be subtracted from the risk-based capital ratio in order to calculate the excess capital ratio to be used in the regression.

11

For a further explanation about the use of the NII to test for the bank capital channel, see Appendix A.

12

In addition, a measure of the real GDP growth and inflation will be used in order to control for loan demand effects. The GDP (in $) and CPI inflation (in %) have been obtained from Thomson data-stream. Another necessary macroeconomic variable to be used in this study is the Monetary Policy (MP) indicator. The federal funds rate has been chosen to represent the MP stance. Bernanke and Blinder (1992) show that the federal funds rate is a suitable indicator to represent the MP stance of the Fed. The data of this variable have been collected from the website of the FED with a quarterly frequency, by the averaging aggregation method.

Table I Summary statistics of the variables used in the regression. The dependent variable listed in the first

column is the change in the log of gross loans. The following (independent) variables have been normalized around their mean in order to capture asymmetric effects: Excess capital (X) (regulatory capital - capital requirements), liquidity (ratio of short-term investments to total assets) denoted by (Liq) and bank size (lnTA) as the log of total assets. Furthermore, (ΔlnGDP) denotes the change in log of GDP, (inf) is the inflation level and (ΔMP) is the change in the federal funds rate. Panels b and c summarize the descriptive statistics of well- and low-capitalized banks. A low-capitalized bank has a capital ratio below the 10th percentile of the capital ratios observed in the Q4 2007 and well-capitalized banks have a capital ratio above the 90th percentile. A similar procedure has been followed to determine the sub-samples of the liquid and low-liquid banks (see panels d and e).

ΔlnLoans X Liq lnTA ΔNII ΔlnGDP Inf Δ MP

Panel a: descriptive statistics "full sample"; (no. of observations: 115,000)

Mean 0.009 0.000 0.000 0.000 0.000 0.010 0.025 0.000 Median 0.008 -0.015 -0.018 -0.077 0.000 0.011 0.025 0.000 Maximum 0.439 0.943 0.537 7.648 0.052 0.022 0.053 0.005 Minimum -0.466 -0.157 -0.078 -3.108 -0.052 -0.022 -0.016 -0.014 Std. Dev. 0.038 0.060 0.069 1.110 0.002 0.008 0.014 0.004 Skewness -0.08 2.56 1.64 0.75 -0.59 -1.93 -0.89 -1.54 Kurtosis 8.79 16.42 6.65 5.18 39.15 7.91 4.49 5.98

Panel b: descriptive statistics "well-capitalized banks sample"; (no. of observations: 11,400)

Mean 0.005 0.128 0.037 -0.551 0.000 0.010 0.025 0.000 Median 0.005 0.111 0.017 -0.622 0.000 0.011 0.025 0.000 Maximum 0.439 0.943 0.438 2.948 0.034 0.022 0.053 0.005 Minimum -0.466 -0.123 -0.078 -3.033 -0.021 -0.022 -0.016 -0.014 Std. Dev. 0.044 0.082 0.088 0.924 0.002 0.008 0.014 0.004 Skewness 0.01 2.26 1.19 0.28 0.18 -1.93 -0.89 -1.54 Kurtosis 15.04 13.81 4.43 3.14 27.76 7.91 4.49 5.98

Panel c: descriptive statistics "low-capitalized banks sample"; (no. of observations: 11,480)

Mean 0.013 -0.050 -0.028 0.720 0.000 0.010 0.025 0.000 Median 0.012 -0.055 -0.040 0.522 0.000 0.011 0.025 0.000 Maximum 0.229 0.095 0.256 7.648 0.018 0.022 0.053 0.005 Minimum -0.289 -0.115 -0.078 -2.515 -0.034 -0.022 -0.016 -0.014 Std. Dev. 0.037 0.017 0.044 1.307 0.002 0.008 0.014 0.004 Skewness -0.06 1.52 1.35 1.38 -2.40 -1.93 -0.89 -1.54 Kurtosis 5.78 7.38 5.30 7.23 43.65 7.91 4.49 5.98

Panel d: descriptive statistics "liquid banks sample"; (no. of observations: 11,440)

Mean 0.006 0.049 0.098 -0.752 0.000 0.010 0.025 0.000 Median 0.006 0.027 0.088 -0.843 0.000 0.011 0.025 0.000 Maximum 0.439 0.592 0.537 3.818 0.041 0.022 0.053 0.005 Minimum -0.466 -0.093 -0.078 -3.033 -0.023 -0.022 -0.016 -0.014 Std. Dev. 0.050 0.083 0.092 0.925 0.002 0.008 0.014 0.004 Skewness -0.09 1.91 0.60 0.67 0.21 -1.93 -0.89 -1.54 Kurtosis 11.98 8.44 3.30 4.42 33.79 7.91 4.49 5.98

Panel e: descriptive statistics "low-liquid banks sample"; (no. of observations: 11,440)

VI. Results

Table II The results of the regression analysis and robustness checks. Model (1) includes two interaction terms to test

the bank lending channel and the risk behavior of banks. Seasonal dummies have also been employed in all the models. The models have been estimated by using LSDV with cross-sectional fixed-effects. The sample goes from the fourth quarter of 2002 to the fourth quarter of 2012. In addition, robustness check “time dummies” (5) is similar to model (1) but without the macroeconomic factors and the MP effect. And model (6) only differs with model (1) by the inclusion of an additional interaction term in order to test the robustness of the bank lending channel of model (1). Furthermore, the number of stars behind the coefficients indicate the significance level at: ***=1%, **=5% and *=10%

Dependent variable: Quarterly growth rate of lending

Baseline regression Equation 1 Robustness check “Time dummies” Equation 5 Robustness check “Liquidity*MP” Equation 6 Coefficient Standard error Coefficient Standard error Coefficient Standard error 1. Intercept (µi) -0.015*** 0.001 0.005*** 0.000 -0.015*** 0.001 2. Lagged values of Lit 0.428*** 0.012 0.364*** 0.009 0.428*** 0.012 3.Excess capital (t-1) 0.186*** 0.007 0.206*** 0.007 0.186*** 0.007 4.Long run coefficients

Monetary policy (MP) -1.944*** 0.245 -1.954*** 0.245 Inflation (CPI) 0.650 0.103 0.650 0.103 Real GDP growth 1.766* 0.154 1.764* 0.154

5. Excess capital*MP (bank lending channel)

6.MP effect for:

0.662*** 0.165 0.772*** 0.164 0.744*** 0.170

- well-capitalized banks -1.488** 0.981 -1.435** 0.981 - low-capitalized banks -3.778 1.135 -3.661 1.137

7. Change in NII (bank capital channel)

-0.707*** 0.062 -0.448*** 0.062 -0.697*** 0.062

8. Excess capital*GDP (risk behavior of banks)

9. GDP shock effect for:

-1.100*** 0.094 -1.272*** 0.093 -1.103*** 0.094

- well-capitalized banks 0.353* 0.573 0.342* 0.574 - low-capitalized banks 5.105*** 0.588 5.118*** 0.588

10. Liquidity*MP (bank lending channel)

11. MP effect for:

0.232** 0.114

- liquid banks -1.09808* 1.013

- low liquid banks -1.99947 0.823

12. Size -0.016*** 0.001 -0.013*** 0.001 -0.016*** 0.001 13. Liquidity 0.052*** 0.003 0.059*** 0.003 0.052*** 0.003 Standard error of regression 0.034 0.033 0.034

Durbin-Watson statistic 1.978 1.970 1.978

Adj. R2 0.212 0.237 0.212

The findings of the baseline regression model and the additional robustness checks are listed in Table II. A model consisting of lagged values of the dependent variable is also referred to as a dynamic model. Lags of the measured variable (Lit) are added for two major reasons: first, to capture the dynamic effects in lending behavior of banks and second, to get rid of the autocorrelation in the residuals of the model. Furthermore, the lagged dependent variables are highly significant with the expected positive sign (see row 2, Table II). In addition, the model includes excess capital (Xit) as a regressor, and in the third row of Table II it can be derived that the coefficient of excess capital (t-1) is positive and highly significant (p-value: 0.00). This can be interpreted that higher excess capital in a previous quarter (t-1) would induce banks to increase their lending in the contemporaneous period (t).

The fed funds rate that is employed as the monetary policy indicator in this thesis has the anticipated negative sign. When the fed funds rate rises by 1% then bank lending decreases with 1.94% all else being equal (see row 4, Table II). It is also interesting to note that the monetary policy effect for well-capitalized banks is much lower than for the low-capitalized banks with coefficient of -1.49 and -3.78, respectively (see row 6, Table II).13 With respect to inflation not enough evidence could be found to consider it as a determinant factor for bank lending. On the other hand, a significant effect was found for the real GDP growth, which was also included as one of the three long-run coefficients. The coefficient of the GDP indicator is positive and significant (p-value: 0.1), giving rise to the notion that the demand for loans is pro-cyclical. In fact, bank lending is expected to increase with 1.77% if real GDP grows by 1% (see row 4, Table II).

The bank lending channel was investigated by including an interaction term combining excess capital with the monetary policy indicator, following the same procedure as Gambacorta and Mistrulli (2004). This is equivalent to testing whether monetary policy effects are equal among banks with different risk-based capital ratios under the null hypothesis. However, the finding of this test is positive and significant as expected (see row 5, Table II). As a result, the conclusion can be drawn that during the period of Q4 2002 to Q4 2012 the bank lending channel was at work in the U.S. This is consistent with the theory that claims that low-capitalized banks will have more difficulties in obtaining uninsured funding compared to well-capitalized banks when the Fed carries out a contractionary policy.

13 _{Important to note is that the coefficient for the sample with low-capitalized banks did not show any significance}

The bank capital channel is somewhat difficult to measure. However, the mechanism of the bank capital channel lies in the shifts in interest rates which impact the profitability of a bank.14 The bank capital channel was tested using the consecutive quarterly differences of the “net interest income to the average earning assets”; see row 7 of Table II for the result and appendix A for the explanation of the use of the abovementioned variable. Surprisingly, the (negative and significant) result of this test is the opposite of what one would expect. This would mean that a 1% increase in NII would decrease bank lending by 0.71%. Nevertheless, if we test for the third and fourth lag of the change in the NII variable, then we would find results that are consistent with the expectations and the theory, namely both signs being positive and significant (p-values: 0.00). This is consistent with the findings of Bernanke and Blinder (1992) who show that an increase in the federal funds rate does not adjust the amount of bank loans in the short term. They show that bank loans fall due to a monetary tightening, but with a significant lag. In fact, they claim that the fall in banks loans does not begin to show up to 6–9 months later, which is completely in line with the findings of this study.

Furthermore, the impact of GDP changes were also tested for low- and well-capitalized banks separately. The results show that bank lending of both sub-samples react positively to an increase in GDP. However, the effect is significantly greater for the sub-sample of low-capitalized banks compared to the well-capitalized sub-sample. The coefficient of the low-well-capitalized banks with respect to the impact of GDP changes is 5.11 (p-value = 0.00) and for the well-capitalized banks it is 0.35 (p-value = 0.10); see row 9, Table II.

The model exhibits a negative correlation between the risk behavior of banks and their lending with a coefficient of -1.10 at a high significance level. This means that a decrease in risk taking would lead to a decrease in bank lending. This has been tested by including the interaction term, which consists of excess capital and the real GDP growth rate, similar to the procedure followed by Gambacorta and Mistrulli (2004). The result of this test (see row 8, Table II) can be interpreted in a way that well-capitalized banks are able to insulate the effects of GDP from their lending activity. This can also be confirmed by looking at the high spread (4.75%) due to a 1% increase in GDP on the bank lending between well-capitalized banks and low-capitalized banks. In fact, the lending behavior of low-capitalized banks is significantly

14

more sensitive to GDP changes in comparison to that of well-capitalized banks. This result seems consistent with the notion that the lending behavior of well-capitalized banks is less impacted by pro-cyclical demand due to the protective disposition of bank capital against credit risk changes.

The bank specific characteristics, the liquidity and size indicators, also indicate a significant correlation with the dependent variable. Furthermore, the goodness of fit (adjusted R2), which statistically measures how well the regression line fits the data, is 21.2%.

VII. Robustness checks

I carry out a number of robustness checks and these results can be found in Table II, under the robustness check columns. However, they are all closely related to the baseline regression model. This means that all robustness checks discussed in this section are estimated using the LSDV with fixed effects and also include the lagged dependent variables. In the first robustness test (4), an additional interaction term is being introduced compared to the baseline regression model; see Gambacorta and Mistrulli (2004). This interaction term is the product of excess capital (X) with inflation (π). This produces the following model:

∑ ∑ ∑ ∑ ∑( ) ∑( ) ∑( ) (4)

The rationale for testing this additional interaction term is the possible relation between bank capital and inflation which might bring endogeneity into play. This is due to the higher level of excess capital when inflation is high and vice versa. Nevertheless, when the test is carried out no changes could be observed in comparison with the results from the original baseline regression model and therefore the interaction term turns out to be insignificant.15

The second robustness test (5) adds a complete set of time-dummies, denoted as ( ), into the regression. The purpose of this test is to investigate whether the model can control for any time variation.

15

Furthermore, it also tests whether the three long-run coefficients (inflation, GDP and the MP indicator), which are pure time variables, are able to capture all the time effects of interest.

∑ ∑( ) ∑( ) (5)

The findings of this test can be found in columns 3 and 4 of Table II in the previous section. The estimated coefficients do not differ much from the results of the original baseline regression model. All coefficients turned out to be highly significant (p-value < 0.01) similar to the estimated coefficients of this model. Interestingly, the adjusted R2 becomes slightly higher due to the inclusion of time dummies. All in all, these results confirm that the estimated coefficients acquired from the baseline regression model are from a reliable disposition.

∑ ∑ ∑ ∑ ∑( ) ∑( ) ∑( ) (6)

The last robustness check (6) to be performed is the original equation with the inclusion of the interaction term combining the MP indicator with the liquidity indicator. The underlying thought behind this test is to confirm whether the asymmetric effects as a result of excess capital remain significant. As a matter of fact, the interaction term turns out to be relevant at a significance level of 5% (p < 0.05), see Table II (column 5 and 6), which is consistent with the findings of Gambacorta (2003), Ehrmann et al. (2003) and Gambacorta and Mistrulli (2004). This means that banks with higher liquidity ratios are in a better position to sustain their lending activity in situations wherein the availability of external funds in the capital markets dries up. This occurs by means of drawing up their liquid funds in order to compensate for the concerning shortfall in funds.

could be compensated easily with the liquid assets owned by the bank before reaching out to the capital reserves. As a result, the bank capital reserves may remain unaltered if the liquid assets are high enough.

VIII. Conclusion

The main purpose of this thesis was to investigate whether the existence of a bank lending channel and a bank capital channel could be detected in the U.S. during the period starting from Q4 2002 to Q4 2012. This study finds evidence of a bank lending channel at a high significance level, meaning that constrained banks will find themselves forced to adjust their lending in consequence to a change in the monetary policy stance. In fact, this result indicates that the markets are imperfect and thus implies a failure of the Modigliani-Miller theorem for banks.

The bank capital channel seems to be at work as well. However, the response of bank lending due to the mechanism of the bank capital channel does not transmit immediately. As a matter of fact, shocks to bank‟s profits, which will eventually affect bank lending due to their impact on the bank‟s equities, have been found to exist but with a lag in time. The effect on bank loans with a significant lag in time has also been documented by Bernanke and Blinder (1992).

Overall, excess risk-based capital seems to be relevant and significant in this study and compel banks to adopt policies (i.e. adjust lending) to avoid jeopardizing the bank‟s health and solvency. Interestingly, the MP effects seemed to have a significant impact on bank lending. However, by testing the sub-samples individually, no evidence was found to claim that low-capitalized banks would be more affected by the MP effects compared to the well-capitalized banks.

The limitation to the research done in this thesis originates from the procedure which I use to measure the bank capital channel. I do this measurement by taking the consecutive quarterly differences of net interest income as a percentage of average earning assets. However, most of the articles related to my topic test the bank capital channel on basis of the maturity mismatch between assets and liabilities. Due to changes in the interest rates this maturity mismatch would consequently lead either to an increase or to a decrease in profits. Even though the methodology based on the maturity mismatch would be more accurate and appropriate, the outcome of the different methodologies should be similar. Furthermore, the use of the LSDV estimator can be considered by some academics to be an inappropriate method for estimating dynamic panel data. Most of the studies related to this topic use the Instrumental Variables (IV) method or the GMM suggested by Arrelano and Bond (1991). However, other works in the field of econometrics (see Roodman (2006)) advocate that the use of the LSDV estimator is appropriate in the presence of large (T) as it would lead to the removal of the dynamic panel bias.

*Acknowledgement*

The writing of this report has been one of the most significant and valuable challenges I have faced in my academic period. Without the support, patience and guidance of the following people, this research would not have been completed. Therefore, I would like to express my deepest gratitude to them all.

First of all, I would like to thank my supervisor prof. dr. K.F. Roszbach. Without his academic expertise and professional guidance, I would not have been able to finish this research. I would like to thank him for being always ready to answer my questions in spite of his heavy schedule.

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Appendix A

Reasoning behind the use of the change in NII to test for the bank capital channel

The bank capital channel is a mechanism which suggests that shifts in interest rates affect the profitability of the banking sector, and in turn its capital accumulation, and eventually impact the lending behavior of banks. This occurs because banks have on average a positive income gap, meaning that their assets have a longer duration than their liabilities, and this leads to an exposure to interest rate risk. This means that when the interest rate rises the interest to be received from the assets which are fixed remain unchanged as banks lend long and borrow short. However, the funding on the liquidity side will change making it more expensive to finance the assets, which will lead to a decrease in Net interest Income (NII). Two important assumptions must be made in order to validate this theory. First, the Modigliani-Miller theorem must be incorrect implying that bank capital must rely on imperfect markets for bank equity. Second, for the bank capital channel to be at work the existence of maturity transformation at banks must be assumed. The latter assumption is the driving factor behind the mechanism in which banks bear a cost when an increase in interest rates occurs and vice versa. In literature, most of the articles which test the bank capital channel use the maturity transformation of the banks (e.g. Gambacorta and Mastrulli, 2004). They use the following model in their study to calculate the maturity transformation:

∑

∑ *100 (1)

where Aj (Lj ) is the amount of assets (liabilities) of j months-to-maturity and χj (ζj ) measures the increase in interest on assets (liabilities) of class j due to a one per cent increase in the key interest rate (r= 0.01). multiplied by MP (federal funds rate) is the interaction term that the article uses to investigate whether the bank capital channel exists or not. They basically calculate the “maturity transformation cost per unit of asset computed for a 1% increase in MP”. Important to note is that the researchers had access to a large database of Italian banks offered by the Italian central bank.

However, the maturity transformation of a bank is basically the same as the standardized adjusted maturity gap ( ) divided by the average earning assets of a bank, see equation (2):

(∑ ∑ ) (2)

respectively. & are the sensitivities of various interest rates of assets and liabilities with respect to changes in the reference rate and indicates the period, expressed as a fraction of the year, from today until the maturity or re-pricing date of the jth asset. Thus, the change in NII is a result of the standardized adjusted maturity gap multiplied by a change in the key interest rate ( ).

Gambacorta & Mistrulli (2004) use the following interaction term to test the bank capital channel:

t-1 (3)

Appendix B

Table III Summary statistics of the variables. The symbols between brackets under the variables indicate the

unit i.e. dollars or percentages. Excess capital (EC) is calculated by the regulatory capital minus the capital requirements; liquidity (liq) is the ratio of short-term investments against total assets. TA is total assets. Loans/TA is the percentage of total assets the bank issued as Loans. NII is the net interest income divided by average earning assets. Panels b and c summarize the descriptive statistics of well- and low-capitalized banks. A low-capitalized bank has a capital ratio below the 10th percentile of the capital ratios observed in Q4 2007 and well-capitalized banks have a capital ratio above the 90th percentile. A similar procedure has been followed to determine the sub-samples of the liquid and low-liquid banks (see panels d and e).

Loans (in $) TA (in $) loans/TA (in %) Liq (in %) EC (in %) NII (in %) GDP (in $) Inf (in %) MP (in %)

Panel a: descriptive statistics "full sample"; (no. of observations: 117,875)

Mean 293,354 444,678 64.47 7.77 8.71 4.13 13,584 2.47 1.81 Median 81,775 127,227 65.00 5.97 7.16 4.07 13,952 2.42 1.02 Maximum 216,138,690 345,088,616 96.43 61.43 102.99 28.05 15,864 5.30 5.26 Minimum 3,249 5,257 35.01 0.00 -7.47 0.00 10,767 -1.62 0.07 Std. Dev. 3,354,819 5,186,284 11.57 6.88 6.05 0.82 1,415 1.34 1.87 Skewness 43.14 44.30 -0.12 1.63 2.55 4.06 -0.43 -0.88 0.78 Kurtosis 2,149.98 2,277.80 2.41 6.62 16.40 79.64 2.20 4.58 2.08

Panel b: descriptive statistics "well-capitalized banks sample"; (no. of observations: 11,685)

Mean 73,158 127,689 56.01 11.49 21.48 4.03 13,584 2.47 1.81 Median 41,153 75,132 54.35 9.46 19.74 4.00 13,952 2.42 1.02 Maximum 1,239,799 3,015,844 96.01 51.58 102.99 16.50 15,864 5.30 5.26 Minimum 3,800 5,920 35.03 0.00 -3.56 1.02 10,767 -1.62 0.07 Std. Dev. 106,468 188,977 11.48 8.80 8.17 1.00 1,415 1.34 1.87 Skewness 5.45 7.58 0.77 1.18 2.26 3.54 -0.43 -0.88 0.78 Kurtosis 46.03 89.59 3.34 4.41 13.76 37.81 2.20 4.58 2.08

Panel c: descriptive statistics "low-rcapitalized banks sample"; (no. of observations: 11,685)

Mean 1,129,808 1,706,396 72.90 4.91 3.69 4.14 13,584 2.47 1.81 Median 168346 235,103 74.10 3.70 3.24 4.06 13,952 2.42 1.02 Maximum 216,138,690 345,088,616 93.88 33.40 14.21 10.12 15,864 5.30 5.26 Minimum 13,493 18,123 36.99 0.00 -2.52 2.05 10,767 -1.62 0.07 Std. Dev. 10,833,692 16,943,837 9.26 4.43 1.65 0.76 1,415 1.34 1.87 Skewness 15.42 15.52 -0.56 1.44 1.43 1.81 -0.43 -0.88 0.78 Kurtosis 248.42 253.29 3.26 5.64 6.85 11.67 2.20 4.58 2.08

Panel d: descriptive statistics "liquid banks sample"; (no. of observations: 11,726)

Mean 70,001 116,893 56.86 17.53 13.56 4.00 13,584 2.47 1.81 Median 33,810 60,312 56.18 16.59 11.42 3.95 13,952 2.42 1.02 Maximum 4,712,489 5,948,593 89.51 61.43 67.92 9.01 15,864 5.30 5.26 Minimum 3,800 5,920 35.01 0.00 -0.55 1.35 10,767 -1.62 0.07 Std. Dev. 217,132 300,657 10.15 9.17 8.32 0.81 1,415 1.34 1.87 Skewness 14.36 12.64 0.28 0.60 1.91 0.54 -0.43 -0.88 0.78 Kurtosis 244.80 197.32 2.53 3.30 8.44 4.48 2.20 4.58 2.08

Panel e: descriptive statistics low-liquid banks sample"; (no. of observations: 11,726)

0 20 40 60 80 100 120 140 160 180 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 N o . o f b an k fa ilu res Years

Bank failures in the U.S. from 2002 - 2012

Appendix C

Table IVCorrelation matrix of the variables used in the regression