The relationship between liquidity risk and credit risk in banks

This paper investigates the relationship between the two major sources of bank default risk: liquidity risk and credit risk. We use a sample of virtually all US commercial banks during the period 1998–2010 to analyze the relationship between these two risk sources on the bank institutional-level and how this relationship influences banks’ probabilities of default (PD). Our results show that both risk categories do not have an economically meaningful reciprocal contemporaneous or time-lagged relationship. However, they do influence banks’ probability of default. This effect is twofold: whereas both risks separately increase the PD, the influence of their interaction depends on the overall level of bank risk and can either aggravate or mitigate default risk. These results provide new insights into the understanding of bank risk and serve as an underpinning for recent regulatory efforts aimed at strengthening banks (joint) risk management of liquidity and credit risks.     

Credit risk transfer and financial sector stability

In this paper, we study credit risk transfer (CRT) in an economy with endogenous financing (by both banks and non-bank institutions). Our analysis suggests that the incentive of banks to transfer credit risk is aligned with the regulatory objective of improving stability, and so the recent development of credit derivative instruments is to be welcomed. Moreover, we find the transfer of credit risk from banks to non-banks to be more beneficial than CRT within the banking sector. Intuitively, this is because it allows for the shedding of aggregate risk which must otherwise remain within the relatively more fragile banking sector. Therefore, regulators should act to maximize the benefits from CRT by encouraging the development of instruments favorable to the cross-sectoral transfer of aggregate credit risk (including basket credit derivatives such as collateralized debt obligations). Finally, we derive the optimal regulatory stance for banks relative to non-bank financial institutions. We show that a level playing field approach is sub-optimal. Regulatory stances should be set to actively encourage cross-sector CRT, first because of the higher fragility of the banking sector and second to induce banks to incur the costs of CRT which otherwise lead them to undertake an insufficient amount of CRT.     

Leverage-induced systemic risk under Basle II and other credit risk policies

We use a simple agent based model of value investors in financial markets to test three credit regulation policies. The first is the unregulated case, which only imposes limits on maximum leverage. The second is Basle II and the third is a hypothetical alternative in which banks perfectly hedge all of their leverage-induced risk with options. When compared to the unregulated case both Basle II and the perfect hedge policy reduce the risk of default when leverage is low but increase it when leverage is high. This is because both regulation policies increase the amount of synchronized buying and selling needed to achieve deleveraging, which can destabilize the market. None of these policies are optimal for everyone: risk neutral investors prefer the unregulated case with low maximum leverage, banks prefer the perfect hedge policy, and fund managers prefer the unregulated case with high maximum leverage. No one prefers Basle II.     

Exploring the components of credit risk in credit default swaps

In this paper, we test the influence of various fundamental variables on the pricing of credit default swaps. The theoretical determinants that are important for pricing credit default swaps include the risk-free rate, industry sector, credit rating, and liquidity factors. We suggest a linear regression model containing these different variables, especially focusing on liquidity factors. Unlike bond spreads which have been shown to be inversely related to liquidity (i.e., the greater the liquidity, the lower the spread), there is no a priori reason that the credit default swap spread should exhibit the same relationship. This is due to the economic characteristics of a credit default swap compared to a bond. Our empirical result shows that all the fundamental variables investigated have a significant effect on the credit default swap spread. Moreover, our findings suggest that credit default swaps that trade with greater liquidity have a wider credit default swap spread.     

Counterparty credit risk and the credit default swap market

Counterparty credit risk has become one of the highest-profile risks facing participants in the financial markets. Despite this, relatively little is known about how counterparty credit risk is actually priced. We examine this issue using an extensive proprietary data set of contemporaneous CDS transaction prices and quotes by 14 different CDS dealers selling credit protection on the same underlying firm. This unique cross-sectional data set allows us to identify directly how dealers' credit risk affects the prices of these controversial credit derivatives. We find that counterparty credit risk is priced in the CDS market. The magnitude of the effect, however, is vanishingly small and is consistent with a market structure in which participants require collateralization of swap liabilities by counterparties.     

Information risk and credit contagion

This paper demonstrates a positive relationship between information risk and the credit contagion effect. We use abnormal changes in the Credit Default Swaps (CDS) spreads to measure the contagion effect, and the dispersion of analyst forecasts as a proxy for information risk. We find that firms with higher information risk suffer a greater contagion effect that occurs in advance to the credit default events. This finding is robust under controls of key firm-specific characteristics and general condition of stock and credit markets.     

Laying off credit risk: Loan sales versus credit default swaps

How do markets for debt cash flow rights, with and without accompanying control rights, affect the efficiency of lending? A bank makes a loan, learns if it needs monitoring, and then decides whether to lay off credit risk. The bank can transfer credit risk by either selling the loan or buying a credit default swap (CDS). With a CDS, the originating bank retains the loan's control rights; with loan sales, control rights pass to the loan buyer. Credit risk transfer leads to excessive monitoring of riskier credits and insufficient monitoring of safer credits. Increases in banks' cost of equity capital exacerbate these effects. For riskier credits, loan sales typically dominate CDS but not for safer credits. Once repeated lending and consequent reputation concerns are modeled, although CDSs remain dominated by loan sales for riskier credits, for safer credits they can dominate loan sales, supporting better monitoring (albeit to a limited extent) while allowing efficient risk sharing. Restrictions on the bank's ability to sell the loan expand the range in which CDSs are used and monitoring is too low.     

Up and down credit risk

This paper discusses the main modeling approaches that have been developed for handling portfolio credit derivatives, with a focus on the question of hedging. In particular, the so-called top, top down and bottom up approaches are considered. We give some mathematical insights regarding the fact that information, namely the choice of a relevant model filtration, is the major modeling issue. In this regard, we examine the notion of thinning that was recently advocated for the purpose of hedging a multi-name derivative by single-name derivatives. We then illustrate by means of numerical simulations (semi-static hedging experiments) why and when the portfolio loss process may not be a ‘sufficient statistic’ for the purpose of valuation and hedging of portfolio credit risk.     

Intra-industry transfer effects of credit risk news: Rated versus unrated rivals

We examine the information transfer effect of bond-rating adjustments on industry rivals. Our research is based on the premise that the transfer effect is influenced by the rated status of rivals, i.e., whether the rival’s debt is rated by any credit rating agency. The results reveal that credit rating adjustments induce different/stronger effects. First, the intra-industry transfer effects (on returns and risk) are stronger on rated rivals than on unrated rivals. Second, the credit risk news produces lower co-movements between the returns of the two types of rivals. Third, the differential effect is stronger in the manufacturing industry, in the riskiest industries and in the industries with the lowest competition levels. Interestingly, our results suggest that credit rating news is more relevant for rivals with access to the public debt market (such as re-rated firms) than for rivals that focus on other sources of funding.     

Credit default swap prices as risk indicators of listed German banks

This paper explores empirically the usefulness of credit default swap (CDS) prices as market indicators. The sample of reference entities consists of large, internationally active German banks and the observation period covers 3 years. By analysing the explanatory power of three risk sources: idiosyncratic credit risk, systematic credit risk and liquidity risk, we gain important insights into modeling the dynamics of CDS spreads. The impact of systematic risk, for example, has three components; one is related to the overall state of the economy, another related to the risk of the internationally active banking sector, and the third is an unobservable systematic factor. Default probabilities, inferred from a tractable reduced form model for CDS spreads, are compared with expected default frequencies from the Moody’s KMV model. The results lend empirical support to the hypothesis that structural models can be less informative than reduced-form models of CDS spreads in the case of banks with major investment banking activities as the leverage loses explanatory power. Although the CDS market appears to have matured over the observation period, during certain periods premiums for liquidity risk can increase substantially thus limiting the value of CDS spreads as market indicators. We conclude that equity prices and CDS premia should be considered together to fully exploit the information content of both market indicators and to mitigate their respective drawbacks.

Contagion effect on bond portfolio risk measures in a hybrid credit risk model

This paper illustrates how modelling the contagion effect among assets of a given bond portfolio changes the risk perception associated to it. This empirical work is developed in a hybrid credit risk framework that incorporates recovery rate risk. Dependence structures among firms and between external shocks affecting firms together are considered. The presence of correlations among firm leverage ratios and the interrelation between default probabilities and recovery rates produces clusters of defaults with low recovery rates. This has a major impact on standard risk measures such as Value-at-Risk and conditional tail expectation. Consequently, an appropriate measurement of the contagion has a tremendous effect on the capital requirement of many financial institutions.     

Evaluating the sovereign and household credit risk in Singapore: A contingent claims approach

This study evaluates the credit risk of the household and government (sovereign) sectors in Singapore using the contingent claims approach (CCA). The CCA model estimates the default probability of both sectors based on the market value of the assets and liabilities of the sectors. Compared to the traditional credit rating system, this model is able to provide numerical estimates of the exposures and default probabilities. We find that from the year 2000 to 2013, variations in the credit risk measures correspond to the economic growth of Singapore. In addition, we suggest that the main factor affecting the credit risks in the government and household sectors in Singapore is the volatility of the assets held by both sectors, given that the asset-to-distress barrier ratios are relatively stable over the past 14 years for both sectors.     

Financial statement recasting and credit risk assessment

This article examines the importance of adjustments to corporate financial statements for credit risk assessment. Prior research has tended to examine individual adjustments one at a time. As correlations among adjustments and control variables may bias inferences when researchers examine a single adjustment and ignore other adjustments, our results provide important new information about previous research by documenting whether or not such bias exists. We find that financial statement recasting adjustments – which aim to better reflect firms' indebtedness, financing costs and recurring earnings than reported financial numbers – are reflected in bond yield spreads and have an economically significant impact on credit pricing and loss forecasting. Among individual adjustment categories, we find that those for off‐balance‐sheet leases, defined benefit pensions and securitized debt have an economically significant impact on credit pricing and loss forecasting.     

Fast simulations in credit risk

We consider the problem of simulating tail loss probabilities and expected losses conditioned on exceeding a large threshold (expected shortfall) for credit portfolios. Our new idea, called the geometric shortcut, allows an efficient simulation for the case of independent obligors. It is even possible to show that, when the average default probability tends to zero, its asymptotic efficiency is higher than that of the naive algorithm. The geometric shortcut is also useful for models with dependent obligors and can be used for dependence structures modeled with arbitrary copulae. The paper contains the details for simulating the risk of the normal copula credit risk model by combining outer importance sampling with the geometric shortcut. Numerical results show that the new method is efficient in assessing tail loss probabilities and expected shortfall for credit risk portfolios. The new method outperforms all known methods, especially for credit portfolios consisting of weakly correlated obligors and for evaluating the tail loss probabilities at many thresholds in a single simulation run.     

The systemic risk of European banks during the financial and sovereign debt crises

European banks became a source of risk to global financial markets during the financial crisis and attention to the European banking sector increased during the sovereign debt crisis. To measure the systemic risk of European banks, we calculate a distress insurance premium (DIP), which integrates the characteristics of bank size, probability of default, and correlation. Based on this measure, the systemic risk of European banks reached its height in late 2011 around €500 billion. We find that this was largely due to sovereign default risk. The DIP methodology is also used to measure the systemic contribution of individual banks. This approach identifies the large systemically important European banks, but Italian and Spanish banks as a group notably increased in systemic importance during the sample period. Bank-specific fundamentals like capital-asset ratios predict the one-year-ahead systemic risk contributions.     

Banking deregulation and credit risk: Evidence from the EU

This paper studies the effect of banking deregulation on credit risk. Its theoretical model shows that a bank is willing to invest more resources in screening borrowers when there is an entry threat, even though loan rates are driven lower. Thus, deregulation may result in improved loan quality and lower credit risk. This result is tested using bank-level balance sheet data and macroeconomic data for the European Union. The data reveal that competition intensified after the completion of the Second Banking Directive, while loan quality improved in most markets. Evidence is found that the loan quality improvement is associated with lower interest margin.     

Growth options,macroeconomic conditions,and the cross section of credit risk

This paper develops a structural equilibrium model with intertemporal macroeconomic risk, incorporating the fact that firms are heterogeneous in their asset composition. Compared with firms that are mainly composed of invested assets, firms with growth options have higher costs of debt because they are more volatile and have a greater tendency to default during recession when marginal utility is high and recovery rates are low. Our model matches empirical facts regarding credit spreads, default probabilities, leverage ratios, equity premiums, and investment clustering. Importantly, it also makes predictions about the cross section of all these features.     

Interpretable selective learning in credit risk

Forecasting credit default risk has been an important research field for several decades. Traditionally, logistic regression has been widely recognized as a solution because of its accuracy and interpretability. Although complex machine learning models may improve accuracy over simple logistic regressions, their interpretability has prevented their use in credit risk assessment. We introduce a neural network with a selective option to increase interpretability by distinguishing whether linear models can explain the dataset. Our methods are tested on two datasets: 25,000 samples from the Taiwan payment system collected in October 2005 and 250,000 samples from the 2011 Kaggle competition. We find that, for most of samples, logistic regression will be sufficient, with reasonable accuracy; meanwhile, for some specific data portions, a shallow neural network model leads to much better accuracy without significantly sacrificing interpretability.     

Valuation of vulnerable American options with correlated credit risk

This article evaluates vulnerable American options based on the two-point Geske and Johnson method. In accordance with the Martingale approach, we provide analytical pricing formulas for European and multi-exercisable options under risk-neutral measures. Employing Richardson’s extrapolation gets the values of vulnerable American options. To demonstrate the accuracy of our proposed method, we use numerical examples to compare the values of vulnerable American options from our proposed method with the benchmark values from the least-square Monte Carlo simulation method. We also perform sensitivity analyses for vulnerable American options and show how the prices of vulnerable American options vary with the correlation between the underlying assets and the option writer’s assets.