threshold paper for bordeaux2

7/28/2019 Threshold Paper for Bordeaux2

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Do Banks suffer from Moral Hazard?

An empirical threshold model of the impact of non-

performing loans on bank lending

Yixin Hou and David Dickinson

Department of Economics

Birmingham Business School

University of Birmingham

Preliminary draft. Please do not quote without the authors permission

This Draft March 2010

Corresponding author

David Dickinson

Department of EconomicsBirmingham Business School

University of Birmingham

Edgbaston

Birmingham B15 2TTUK

Tel +44 (0)121 414 8093

Fax +44 (0)121 414 7380Email: [email protected]


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Abstract

This paper examines how non-performing loans (NPLs) affect

bank lending behaviour. It uses a large multi-country data set of

banks and investigates the influence of NPLs on the amount of

credit which banks grant. We use a threshold model to investigate

this issue and find that lending behaviour is significantly different

below and above a critical threshold level of NPLs. We find that

lending is dependent also on the capital ratio. The thresholds are

different across developed and less-developed financial markets.The paper identifies that the results obtained provide evidence on

whether banks are subject to moral hazard in lending. The impact

of capital regulations also influences the risk-taking behaviour of

banks.

JEL Codes G21, G18, G11


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1. Introduction

Given the recent turbulence in banking and the rise in non-performing loans (NPLs) there is

renewed interest in the impact of NPLs on banks and their behaviour. Of particular interest is the

extent to which deterioration in bank performance leads to moral hazard in that there is an

incentive for banks to take more risk. We investigate this issue by examining the relationship

between bank lending and NPLs. Specifically we use a threshold approach to identify if and how

bank lending behaviour changes as the level of NPLs rises above a threshold value (which is

determined endogenously). By carefully specifying the implications of the moral hazard

hypothesis we are able to use our empirical findings to understand whether it is an issue. We also

control for capital adequacy in order to consider the impact of regulatory controls on our results.

Our study is multi-country and hence we have a number of alterative regulatory regimes in which

to consider our findings and to explain their significance.

Much of the research on NPLs to this time has been directed at their implications for bank failure

and the findings that asset quality is a statistically significant predictor of insolvency and that

banking institutions always have large non-performing loans prior to failure (e.g. Dermirgue-

Kunt 1989, Barr and Siems 1994). Literature focusing on the causes of the NPLs problems

provides a number of explanations for the phenomenon. At the microeconomic level, asymmetric

information and adverse selection, risk preference, risk measurement, corporate governance, have

been put forward to try to explain the causes of non-performing loans. From the macroeconomic

view, the non-performing loans problem is seen to be the consequence of macroeconomic

inefficiency. For example, the long lasting Japanese bad-loan problem since the bubble burst in

1990 is viewed as the consequence of a deflationary slump. The experience of Japan, as well as

that of the 1930s has been the major impetus for the policy responses to the current banking

crisis.

It has been argued that the general impact of NPL is to induce reductions in lending and a flight

to quality (e.g. Bernanke and Gertler, 1994). Hence enterprises, which are financially sound,

suffer liquidity problems and financial distress resulting in bankruptcy, as a result of the drying-

up of bank credit channels. Furthermore the need of banks to re-build capital means that they cut

back more generally on extending credit, causing reduced demand and hence further falls in

economic activity. Alternatively there is the argument that banks engage in more risky lending as


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a result of moral hazard when NPLs rise. In other words, there is an incentive for banks to take

more risks if their financial position is worsening as a result of increased NPLs. This problem

becomes even more acute if there is a perception that banks will be bailed out if they become

insolvent (e.g. Boyd et. al, 1998, Nier and Baumann, 2006). Examining the relationship between

bank lending and their NPLs will provide insights into the potential for credit crunches or moral

hazard problems to appear since we will be able to identify the extent to which banks increase

lending as NPLs increase or reduce.

We start from the premise that the impact of NPLs on bank lending is non-linear. It is quite

normal for banks to experience bad loans as a normal part of their business and hence we would

not expect to observe effects on bank behaviour at normal levels. However once NPLs rise

above this normal operational level banks will need to take action to stabilise their business, by

building capital and adjusting their credit policies to increase loan quality, or take more risk in

order to build funds as a way out of potential insolvency problems. This approach leads us

naturally to adopt a threshold regression technique to model the empirical relationship between

NPLs and bank lending. We find that such an approach has some empirical success and that

there is interesting variation in the threshold level across different regions. Since we also apply

thresholds for bank capital ratios we indentify some interesting interactions between banks

behaviour and regulatory requirements.

The rest of this paper is organised as follows. Section 2 explains our definition of non-performing

loans and highlights why non performing loans can impact on lending behaviour as well as

discussing consequences of non-performing loans on the economy more generally. Section 3 uses

the empirical version of the threshold model to test how the non-performing loan affects banks

loan decision. And section 4 provides interpretation of our results and concluding comments.

2). A Threshold model of the effect of NPLs on Bank Lending

In order to understand the relationship between bank lending and NPLs we need to consider why

it is that NPLs occur. There are both a demand and supply side factors. Clearly not all firms that

borrow will succeed and defaults and bankruptcy are natural economic selection processes. Banks


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will typically lend on the basis of information gathered about the borrower, setting the interest

rate to reflect the risks of lending and any collateral requirements, and managing the risk in a pro-

active way.

Clearly if banks dont operate their business well then we would expect to see a higher proportion

of NPLs. It has been observed that failing banks (those with high NPLs) tend to be located far

from the efficient frontier (Berger and Humphrey (1992), Barr and Siems (1994), DeYoung and

Whalen (1994), Wheelock and Wilson (1994)). There is evidence that even among banks that do

not fail, there is a negative relationship between the non-performing loans and performance

efficiency (Kwan and Eisenbeis (1994), Hughes and Moon (1995), Resti (1995)). Other empirical

studies using supervisory data have supported such a negative relationship. For example,

Peristiani (1996) finds a positive relationship between the cost efficiency and the examiners

ratings of bank management quality. DeYoung (1997) observes a stronger link between the

banks management ratings and their assets quality ratings. Some direct measures of bank cost

and production also show a negative relationships between the NPLs and bank performance

(Berg, Forsund and Jansen (1992), Hughes and Mester(1993)). So we may observe that banks

with high NPLs are likely to be low efficiency banks and that NPLs may be associated with poor

risk management.

The relationship between NPLs and banks lending decisions is likely to be driven by macro as

well as micro factors. Thus changes in NPLs may reflect the economic environment. For

example, an increase in NPLs may signal unanticipated economic deterioration which causes an

upward revision in the probability of loan default. There may be a natural inclination to tighten

credit allocation procedures which has the effect of reducing the amount of credit extended and

an improvement in the quality of assets as bad loans are cleared. Additionally the negative effect

of NPLs on future lending is often associated with the need of banks to build up capital to protect

against loan losses. Finally bank managers may be rewarded according to their relative

performance. If the expectation is that the NPL problem is economy-wide then there will be an

incentive to avoid further losses in order to be seen to be doing better than the market as a whole.

So we would expect to see a relationship between NPLs and bank lending decisions driven by

both market and economy wide factors as well as bank level problems.


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The particular feature of this paper is to consider the interaction between NPLs and bank lending

decisions but in the context of a threshold model. Our rationale for using the threshold approach

is that banks typically make provisions for NPLs up to a specific (normal) level and may also cut

back on lending in response to increases in NPLs but that when they rise above this point the

bank begins to respond more aggressively and indeed to start to take more risk. Such a threshold

will reflect historical factors such as the observed distribution of NPLs (for example banks may

use a particular confidence interval which implies that they infer a small probability (e.g. 5%) of

NPLs rising above this level). Alternatively regulations and the actions of the regulatory agencies

may imply that NPLs up to a certain level require no specific action while beyond the authorities

start to look more closely at the activities of the particular bank. Of course the appropriateness of

the threshold model is an empirical matter which we shall take up in the next section.

The existence of a threshold approach to managing lending may also have implications for the

credit crunch view of the loans market. If there is a substantial increase in the negative impact of

NPLs on new lending after a particular level of NPLs are reached then we would expect to see

credit crunches occurring once this critical level had been reached across the banking sector. In

other words whilst NPLs are at a level considered normal the possibility of a credit crunch would

be rather low but once they went generally above this level the chances are significantly

increased.

We now turn to the framework in which we shall set up our threshold model. We wish to

examine the impact of increasing non-performing loans on credit supplies of commercial banks,

controlling for other factors that affect credit representing demand and supply conditions. For a

simple commercial bank balance sheet, assets are commercial loans and other earning assets and

in addition there is cash and other non-earning assets which typically absorb short-term shocks;

on the liability side, deposits and other short-term funding along with capital are the main

components. Thus, we can conjecture that the loan growth is affected by deposit growth, capital

growth and other earning assets growth.

Thus the supply of loans (Lt) is determined by banks lending capacity, given by deposit growth

rate and factors that influence banks willingness to provide credits the capital-asset ratio and the


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risks as measured by NPLs. It might appear that we have ignored revenue considerations but this

is taken into account through the impact of other earning assets. In other words we would expect

the growth rate of this element of the portfolio to reflect the relative (to lending) benefits of this

class of asset in the portfolio. The basic model is as follows:

1,4,3,2,10, ++++= tititititi NPLGRaOEAGRaCGRaDGRaaLGR (1)

where the index i is the index for individual banks and t is the index for time period. tiLGR ,

is the loan growth rate for each bank in each time period t , tiDGR , is the deposit growth rate,

tiCGR , is the capital growth rate, tiOEAGR , is the other assets growth rate, and 1, tiNPLGR

is non-performing loan growth rate of the previous time period. The balance sheet constraint

implies that there will be an adding-up relationship between the growth rates but note that we

have not included all balance sheet items so this is implicit. In other words shocks to the balance

sheet are absorbed by the non-included items such as cash and non-deposit liabilities. We have

argued above that macroeconomic conditions may also affect loan growth. Rather than specify

the variables we add year dummies to reflect these macro variables which will affect all banks

but vary over time.

We can make conjectures about the signs of the coefficients above. As deposits increase at a

faster rate, lending capacity of the bank increases and hence loan growth should rise. A ceteris

paribus increase in other earning assets growth would be a signal that they are becoming

relatively profitable (their return/risk relationship is more favourable). This would reduce lending

growth. The impact of capital growth would be expected as positive. The reason is that higher

capital gives the bank more capacity to take risk and hence makes more credit available. Finally

an increase in NPLs last period would signal worsening lending conditions with higher associated

risks and a reduction in loan growth as a result.

As indicated we intend to adopt a threshold approach to our empirical analysis. We can also

identify how we might expect our responses to be sensitive to the level of NPLs. However as will

become clear the actual effect is dependent on whether we believe banks are subject to moral

hazard or not. This is going to be very helpful in establishing the implication of our results.


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Firstly we consider the case where banks are not subject to moral hazard. Deposit growth is likely

to exert a smaller positive impact as NPLs rise since banks will use deposit growth to rebuild

their capital and consequently increase credit less. Similarly when the banks are affected by an

increasing non-performing loan problem, they are likely to switch to safer assets, such as

government bonds or treasury bills. As a result of this increased substitution effect, the other

earning asset growth should have larger negative effect on loan growth. The positive impact of

capital growth will be moderated as NPLs increase since banks will be concerned to build a

buffer against loan losses. Finally we would expect that as NPLs increase the NPL growth rate

has a stronger negative effect.

Now suppose that banks are subject to moral hazard in that they take more risk as they suffer

declining performance due to rising NPLs. Increases in deposit growth will now have a larger

effect on lending since banks will choose to make more risky loans in order to generate higher

return. The impact of other earning assets will be reduced as the substitution effect fails to

operate even though there is an increased risk of making loans. Finally we would see an increase

in lending to be enhanced by capital growth as NPLs increase and banks can offset higher capital

by taking more risk. Finally we would expect to see a declining effect of NPL growth on credit.

Beyond this basic specification we also propose to consider an additional threshold based on the

banks capital ratio. Under the Basle Accord II framework banks are required to adjust their

capital to reflect the riskiness of the assets they hold. Increases in NPLs would be expected to

impact on capital requirements as the riskiness of loans increases. According to the Basle Accord

II, the target ratio of capital to risk weighted assets is set at 8%. Not surprisingly, given the need

to have a buffer-stock of capital we observe from our data set that the mean capital ratios in our

samples are all above the required 8%. We define a dummy variable which identifies if the

capital ratio of the bank is above or below a specific value. We then run an adjusted regression

equation which is:

1,,6,5

4,3,2,10,

++

++++=

tititi

titititi

NPLGRDummyaDummya

NPLGRaOEAGRaCGRaDGRaaLGR1-i,t

(2)


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The point of equation (2) is that we expect loan growth to be influenced directly by the effective

capital ratio, as well as allowing the response of lending to growth of NPLs to be influenced by

whether the bank has met the effective capital ratio. What is exactly this effective capital ratio is

an empirical question.

3). The Empirical analysis of Lending and NPLs

In this paper we use a panel of individual banks balance sheet data across a range of countries.

We use the standard BIS definition of NPLs1. The argument for this is firstly that the standard

definition makes it possible to compare the non-performing loan problem across countries and

banks. Secondly, the BIS definition is a prudential definition for NPLs, which includes loans with

loss uncertainty as well as those for which a loss has been incurred and hence should be a

reasonable guide to the banks estimate of how large is the NPL problem.

Since the variables we use in the regression as explanatory variables are potentially endogenous

as they are simultaneously determined through banks balance sheet constraints, we apply the

method of two-stage least squares method using instrumental variables (see Wooldridge (2002)

for details of this technique in a panel context). We assume banks behaviour is continuous and

they re-balance the portfolio to the desired level each period (which is reasonable in the context

of our use of annual data).

Threshold regression techniques are used to address the question of how bank credit decisions

relate to the level of NPLs. Threshold models have a wide variety of applications in economics.

Applications include separating and multiple equilibrium, sample split, mixture models,

1

According to the BIS, the standard loan classifications are defined as follows:Passed: Solvent loans; SpecialMention: Loans to enterprises which may pose some collection difficulties, for instance, because of continuing

business losses; Substandard: Loans for which interest or principal payments are longer than three months in arrears

of lending conditions. The banks make 10% provision for the unsecured portion of the loans classified as

substandard; Doubtful: Full liquidation of outstanding debts appears doubtful and the accounts suggest that therewill be a loss, the exact amount of which cannot be determined as yet. Banks make 50% provision for doubtful loans;

Virtual Loss and Loss (Unrecoverable): Outstanding debts are regarded as not collectable, usually loans to firms

which applied for legal resolution and protection under bankruptcy laws. Banks make 100% provision for loss loans.

E use the last three of these classes to define NPLs


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switching models, etc. Hansen (2000) argues that the understanding of threshold models is a

preliminary step in the development of statistical tools to handle more complicated statistical

structures.

The development of threshold regression models can be traced back to Dagenais (1969). He uses

the threshold regression technique to analysis the step-like-time-path discontinuous character of

durable goods. Hansen (1999) develops the panel threshold regression methods for non-dynamic

panels with individual-specific fixed effects. In the model, the observations are divided into two

regimes depending on whether the threshold variable itq is smaller or larger than the threshold

. The two regimes are distinguished by differing regression coefficients 21 , . He shows that

for any given , the slope coefficient can be estimated by ordinary least squares (OLS). The

threshold level is identified as the one which generates minimisation of the sum of squared errors.

Hansens (1999) threshold model is based on a balanced panel. Khan and Senhadji (2001) extend

the technique to an unbalanced panel. The estimation is carried out using the conditional least

squares and the threshold is determined at the point that minimises the sum of squared

residuals. Caner and Hansen (2004) further develop a model with endogenous variables but an

exogenous threshold variable. We can extend their model to be used for panel data with

individual-specific fixed effects. The estimation is sequential. The first step is to estimate the

reduced form parameters by least squares. The second step is to estimate the threshold using

predicted values of the endogenous variables itz . And the third step is to estimate the slope

parameters 1 and 2 by 2SLS on the split samples implied by the estimate of . The 2SLS

estimator for is the minimiser of the sum of squared errors. Caner and Hansen (2004)

demonstrate that if the threshold variable is exogenous, the estimator is consistent. However, it is

difficult to know if it is efficient as other estimators are possible and efficiency is difficult to

establish in nonregular models.

In our estimation model, there are two thresholds, the non-performing loan rate and capital ratio.

As both of the two variables are treated exogenous in our model, we can follow Caner and

Hansen (2004) three step method. In the first step, we estimate the fitted value of


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tititi CGRDGROEAGR ,,, ,, by using the lagged variables 1,1,1, ,, tititi CGRDGROEAGR as

the instruments. The second step is to estimate the thresholds using predicted values of the

endogenous variables''

,

',, ittiit CGRDGROEAGR . And the third step is to estimate the parameters

by 2SLS on the split samples implied by the estimates. And the 2SLS estimator is the maximiser

of QLwhere ==2

1

2 )ln(j jj

nQL .For the full sample, the threshold for itNPLR is chose

within the interval of ( )%15%,0 with an increment of %1.0 . And for each level of itNPLR , we

estimate different capital ratio level within the interval of ( )%20%,8 2 with an increment of

%1.0 . It yields totally 18000 estimated QL s, and the best estimator is chosen by the

combination of the two thresholds where the QL is maximised.3

Specifically we determine the thresholds by estimating the models as follows:

threshold paper for bordeaux2

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