The relationship between liquidity risk and credit risk in banks
Introduction
What is the relationship between liquidity risk and credit risk in financial institutions? Classic theories of the microeconomics of banking support the view that liquidity risk and credit risk are closely linked. Both industrial organization models of banking, such as the Monti–Klein framework, and the financial intermediation perspective in a Bryant (1980) or Diamond and Dybvig (1983) setting, suggest that a bank’s asset and liability structures are closely connected, especially with regard to borrower defaults and fund withdrawals. This does not only hold true for banks’ balance sheet business but also for the lending and funding business conducted through off-balance sheet items, as shown by e.g. Holmström and Tirole (1998) or Kashyap et al. (2002). Building on these models, a body of literature has recently evolved focusing on the interaction of liquidity risk and credit risk and the implications for bank stability. Papers such as Goldstein and Pauzner, 2005, Wagner, 2007, Cai and Thakor, 2008, Gatev et al., 2009, Acharya et al., 2010, Acharya and Viswanathan, 2011, Gorton and Metrick, 2011, He and Xiong, 2012a, He and Xiong, 2012b, and Acharya and Mora (in press) look into the matter from various angles and derive, mostly from a theoretical perspective, results which show the influence liquidity and credit risk have on each other and also how this interaction influences bank stability.
Anecdotal evidence from bank failures during the recent financial crisis further supports these theoretical and empirical results. Perhaps only indicative in nature, official reports of the FDIC and OCC about the reasons for bank failures (so called “Material Loss Reports”1) explicitly state that the majority of commercial bank failures during the recent crisis were partly caused by the joint occurrence of liquidity risks and credit risks. Also, Switzerland-based money center bank UBS addressed the main causes for its substantial losses and subsequent financial distress in the wake of the 2007/2008 financial crisis in a 2008 report to its shareholders2 as follows: “UBS funding framework and related approach to balance sheet management were significant contributors to the creation of UBS’s Subprime exposure” (p. 36). Apparently, the bank did not differentiate between liquid and illiquid assets and the respective term funding and thereby also disregarded the credit risks of the assets. Albeit this evidence is only of anecdotal nature, it might be a sign that the joint occurrence of liquidity and credit risks plays a tremendous role for banks and their stability, and that banks do not account for this joint occurrence in their risk management systems. This assumption is supported by recent regulatory changes, like the Basel III framework and its Liquidity Coverage Ratio (LCR) and Net Stable Funding (NSF) Ratio, or the Dodd–Frank Act with its proposed liquidity stress-tests. Yet, in spite of this alleged importance and the ample theoretic evidence behind it, no paper has so far analyzed the relation between liquidity risk and credit risk on a broad range and in its different dimensions across the banking sector. As a consequence, many important questions regarding this topic remain unanswered: what is the general relationship between liquidity risks and credit risks in banks? Do liquidity and credit risk jointly influence banks’ probability of default (PD)? If so, do banks manage both risks together?
We try to answer these questions by empirically analyzing the relationship between liquidity risk and credit risk in 4046 non-default and 254 default US commercial banks over the period 1998:Q1 to 2010:Q3, using a large variety of different subsamples and tests. We use two main liquidity and credit risk proxy variables.3 We develop a liquidity risk (LR) proxy variable which measures short-term funding risks of banks, as represented by the relationship of short-term obligations to short-term assets, including off-balance sheet items as for example unused loan commitments. We thereby account for classic “bank run” risks. For credit risk (CR) we develop a proxy variable measuring the unexpected loan default ratio of a bank, as represented by the net loan losses in the current period to the allowances for these loan losses recorded in the previous period. This variable captures the current riskiness of a banks’ loan portfolio and the accuracy of a bank’s risk management to anticipate near-term loan losses.
In the first step of our analysis we measure the general relationship between liquidity and credit risk in banks. We are specifically interested in whether or not there is a reciprocal relationship between the two factors, i.e. whether or not liquidity risk influences credit risk or vice versa, and if this relationship is positive or negative. Our results show that there is no reliable relationship between liquidity risk and credit risk in banks. We distinguish between the different dimensions of liquidity and credit risk using several proxy variables and control for other possible influence factors in a large number of robustness tests. Furthermore, we incorporate different econometric approaches: a simultaneous equations model controlling for both contemporaneous and lagged influences between liquidity risk and credit risk, and a panel-VAR model together with a correlation analysis to separately control for contemporaneous and lagged relationships. Although the results in some cases show statistical significances, the economic influence is at best marginal.
Given that there is no reliable relationship between the two risk factors across banks, we ask in the second part of our analysis if liquidity risk and credit risk individually and also jointly contribute to bank default risk. For this purpose we include our main proxy variables for liquidity risk and credit risk, as well as the interaction between both risks in a multivariate logistic regression model to determine their contributions to banks’ PD. Our results show that both liquidity risk and credit risk individually influence banks’ PD. Furthermore, we find that the interaction between the two risk categories has an additional effect on bank PD. Surprisingly, this effect varies for banks with different levels of PD: the joint occurrence of liquidity and credit risks has a PD-aggravating effect for banks with a PD of 10–30%. In contrast, we find that it is mitigating for banks with a high PD of 70–90%. Apparently, the joint effect of simultaneously high liquidity and credit risk has a dampening effect on the otherwise PD-aggravating individual effects of the two risk categories in banks which are close to default. These results might point to a gambling for resurrection behavior. Taken together, our findings suggest that there is an important relation between liquidity risk and credit risk which affects the overall probability of bank default.
Our study contributes to two strands of literature. For liquidity risk, these are the seminal works of Bryant (1980) and Diamond and Dybvig (1983) which have been extended, refined and applied numerous times by e.g. Calomiris and Kahn, 1991, Diamond and Rajan, 2001, and most recently Berger and Bouwman (2009).4 The credit risk studies we build on are too numerous to be mentioned in full; the most recent examples include e.g. Illueca et al., 2008, Laeven and Levine, 2009, Foos et al., 2010, Houston et al., 2010, and also Rajan and Winton, 1995, Boot, 2000, and Berger and Udell (2004) (a very in-depth overview of earlier studies is provided by e.g. Altman and Saunders, 1998). The remainder of the paper is structured as follows. Section 2 provides the theoretical background for our analysis. Section 3 describes the data including our proxy variables for liquidity and credit risk and presents descriptive statistics. Section 4 presents the results and Section 5 concludes.
Section snippets
The reciprocal relationship between liquidity risk and credit risk
Over the past 50–60 years, a tremendous amount of literature has dealt with banks’ liquidity and credit risks. Explanations for the way banks work and their major risk and return sources are given by two major research strands regarding the microeconomics of banking: the classic financial intermediation theory, most prominently represented by the Bryant (1980) and Diamond and Dybvig (1983) models and their extensions (such as Qi, 1994, or Diamond, 1997), and also by the industrial organization
Data and sample selection
For all bank balance sheet, profit & loss account, and off-balance sheet items we use official FFIEC Call Report data on a quarterly basis, publicly obtainable through the Federal Reserve Bank of Chicago. Banks in our dataset are solely US-based and -held banks. We deliberately exclude all US-based and -chartered subsidiaries of foreign bank holding companies, as well as all thrifts and money center banks to obtain a more homogeneous bank sample in terms of ownership and governance. All banks
The Relationship between liquidity risk and credit risk
In this subsection we investigate the direct relationship between liquidity risk and credit risk in banks using our risk proxy variables. First, we briefly explain the methodology used in our analyses. This is followed by an analysis of the general relationship between liquidity risk and credit risk. Finally, we examine the relationship subdividing banks in terms of risk.
Conclusion
Liquidity risk and credit risk are the two most important factors for bank survival. This study investigates the relationship between these factors in virtually all commercial banks in the US over the period 1998:Q1 to 2010:Q3. We show that each risk category has a significant impact on bank default probability. We also document that the interaction of both risk categories significantly determines banks’ PDs, albeit in different fashions. Whereas the interaction between liquidity risk and
Acknowledgments
Part of the research was conducted while Christian Rauch was visiting Moore School of Business at the University of South Carolina. The authors would like to thank an anonymous referee for valuable input, as well as Allen N. Berger, Christa Bouwman, Andreas Hackethal, Michalis Haliassos, Karolin Kirschenmann, Jan-Pieter Krahnen, Lars Norden, Raluca Roman, Sascha Steffen, Mark Wahrenburg, Liying Wang and participants at the Financial Management Association, Southern Finance Association,
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