Showing papers on "Credit risk published in 2015"

Systemic risk and stability in financial networks

Abstract: This paper argues that the extent of financial contagion exhibits a form of phase transition: as long as the magnitude of negative shocks affecting financial institutions are sufficiently small, a more densely connected financial network (corresponding to a more diversified pattern of interbank liabilities) enhances financial stability. However, beyond a certain point, dense interconnections serve as a mechanism for the propagation of shocks, leading to a more fragile financial system. Our results thus highlight that the same factors that contribute to resilience under certain conditions may function as significant sources of systemic risk under others. (JEL D85, E44, G21, G28, L14)

Structural versus reduced form models: a new information based perspective

Abstract: This paper compares structural versus reduced form credit risk models from an information based perspective. We show that the difference between these two model types can be characterized in terms of the information assumed known by the modeler. Structural models assume that the modeler has the same information set as the firm’s manager—complete knowledge of all the firm’s assets and liabilities. In most situations, this knowledge leads to a predictable default time. In contrast, reduced form models assume that the modeler has the same information set as the market—incomplete knowledge of the firm’s condition. In most cases, this imperfect knowledge leads to an inaccessible default time. As such, we argue that the key distinction between structural and reduced form models is not whether the default time is predictable or inaccessible, but whether the information set is observed by the market or not. Consequently, for pricing and hedging, reduced form models are the preferred methodology.

Determinants of Default in P2P Lending

Abstract: This paper studies P2P lending and the factors explaining loan default. This is an important issue because in P2P lending individual investors bear the credit risk, instead of financial institutions, which are experts in dealing with this risk. P2P lenders suffer a severe problem of information asymmetry, because they are at a disadvantage facing the borrower. For this reason, P2P lending sites provide potential lenders with information about borrowers and their loan purpose. They also assign a grade to each loan. The empirical study is based on loans’ data collected from Lending Club (N = 24,449) from 2008 to 2014 that are first analyzed by using univariate means tests and survival analysis. Factors explaining default are loan purpose, annual income, current housing situation, credit history and indebtedness. Secondly, a logistic regression model is developed to predict defaults. The grade assigned by the P2P lending site is the most predictive factor of default, but the accuracy of the model is improved by adding other information, especially the borrower’s debt level.

Quantitative risk management: Concepts, techniques and tools: Revised edition

Abstract: This book provides the most comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management. Whether you are a financial risk analyst, actuary, regulator or student of quantitative finance, Quantitative Risk Management gives you the practical tools you need to solve real-world problems. Describing the latest advances in the field, Quantitative Risk Management covers the methods for market, credit and operational risk modelling. It places standard industry approaches on a more formal footing and explores key concepts such as loss distributions, risk measures and risk aggregation and allocation principles. The book’s methodology draws on diverse quantitative disciplines, from mathematical finance and statistics to econometrics and actuarial mathematics. A primary theme throughout is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. Proven in the classroom, the book also covers advanced topics like credit derivatives. Fully revised and expanded to reflect developments in the field since the financial crisis Features shorter chapters to facilitate teaching and learning Provides enhanced coverage of Solvency II and insurance risk management and extended treatment of credit risk, including counterparty credit risk and CDO pricing Includes a new chapter on market risk and new material on risk measures and risk aggregation

Monetary Policy, Risk-Taking, and Pricing : Evidence from a Quasi-Natural Experiment

Abstract: We analyse the impact of monetary policy on bank risk-taking and pricing. Bolivia provides us with an excellent experimental setting to identify this impact. Its small economy is not synchronized with the US economy but its banking system is almost fully dollarized. Consequently the US federal funds rate is the appropriate measure of monetary policy. We study the impact of the federal funds rate on the riskiness and pricing of new bank loans granted in Bolivia between 1999 and 2003, a period of significant variation in the federal funds rate. We find robust evidence that a decrease in the US federal funds rate prior to loan origination raises the monthly probability of default on individual bank loans. We also find that initiating loans with a subprime credit rating or loans to riskier borrowers with current or past non-performance become more likely when the federal funds rate is low. However, loan spreads do not increase, seemingly even decrease, in changes in the probability of default. Hence banks do not seem to price the additional risk taken. Furthermore, banks with more liquid assets and less funds from foreign financial institutions take more risk when the federal funds rate is low, and reduce loan spreads more despite the additional risk they seemingly take.

Bilateral counterparty risk under funding constraints—part ii: cva

Abstract: The correction in value of an over-the-counter derivative contract due to counterparty risk under funding constraints is represented as the value of a dividend-paying option on the value of the contract clean of counterparty risk and excess funding costs. This representation allows one to analyze the structure of this correction, the so-called Credit Valuation Adjustment (CVA for short), in terms of replacement cost/benefits, credit cost/benefits, and funding cost/benefits. We develop a reduced-form backward stochastic differential equations (BSDE) approach to the problem of pricing and hedging the CVA. In the Markov setup, explicit CVA pricing and hedging schemes are formulated in terms of semilinear partial differential equations.

Corporate social responsibility and Eurozone corporate bonds: The moderating role of country sustainability

Abstract: In this paper, we empirically examine whether superior performance in corporate social responsibility (CSR) results in lower credit risk, measured by credit ratings and zero-volatility spreads (z-spreads). We are especially interested in how the environmental, social, and governance (ESG) related performance of the corresponding countries moderates this relationship. We find only weak evidence that superior corporate social performance (CSP) results in systematically reduced credit risk. However, we do find strong support for our hypothesis that a country’s ESG performance moderates the CSP–credit risk relationship. Superior CSP is regarded as risk-reducing and rewarded with better ratings and lower z-spreads only if it is recognized by the environment. In addition, we find a reduction of corporate bonds’ z-spreads by approx. 9.64 basis points if the CSP of a company mirrors the ESG performance of the country it is located in.

Predictions of Default Probabilities in Structural Models of Debt

Abstract: The author investigates corporate bond default probabilities associated mainly with two structural models. Although the models offer alternate probability and recovery rate predictions given changes to common inputs such as volatility and debt maturity, the models reasonably describe actual default probability curves across ratings categories for long horizons. At short horizons, however, the models underpredict defaults and yield spreads. Adding a jump condition to the asset value process may provide a solution.

Bilateral counterparty risk under funding constraints—part i: pricing

Abstract: This and the follow-up paper deal with the valuation and hedging of bilateral counterparty risk on over-the-counter derivatives. Our study is done in a multiple-curve setup reflecting the various funding constraints (or costs) involved, allowing one to investigate the question of interaction between bilateral counterparty risk and funding. The first task is to define a suitable notion of no arbitrage price in the presence of various funding costs. This is the object of this paper, where we develop an “additive, multiple curve” extension of the classical “multiplicative (discounted), one curve” risk-neutral pricing approach. We derive the dynamic hedging interpretation of such an “additive risk-neutral” price, starting by consistency with pricing by replication in the case of a complete market. This is illustrated by a completely solved example building over previous work by Burgard and Kjaer.

Preface [Quantitative Risk Management: Concepts, Techniques and Tools Revised edition]

Abstract: This book provides the most comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management. Whether you are a financial risk analyst, actuary, regulator or student of quantitative finance, Quantitative Risk Management gives you the practical tools you need to solve real-world problems. Describing the latest advances in the field, Quantitative Risk Management covers the methods for market, credit and operational risk modelling. It places standard industry approaches on a more formal footing and explores key concepts such as loss distributions, risk measures and risk aggregation and allocation principles. The book’s methodology draws on diverse quantitative disciplines, from mathematical finance and statistics to econometrics and actuarial mathematics. A primary theme throughout is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. Proven in the classroom, the book also covers advanced topics like credit derivatives. Fully revised and expanded to reflect developments in the field since the financial crisis Features shorter chapters to facilitate teaching and learning Provides enhanced coverage of Solvency II and insurance risk management and extended treatment of credit risk, including counterparty credit risk and CDO pricing Includes a new chapter on market risk and new material on risk measures and risk aggregation

Peer-to-Peer Crowdfunding: Information and the Potential for Disruption in Consumer Lending

Abstract: Can peer-to-peer lending (P2P) crowdfunding disintermediate and mitigate information frictions in lending such that choices and outcomes for at least some borrowers and investors are improved? I offer a framing of issues and survey the nascent literature on P2P. On the investor side, P2P disintermediates an asset class of consumer loans, and investors seem to capture some rents associated with the removal of the cost of that financial intermediation. Risk and portfolio choice questions linger prior to any inference. On the borrower side, evidence suggests that proximate knowledge (direct or inferred) unearths soft information, and by implication, P2P should be able to offer pricing and/or access benefits to potential borrowers. However, social connections require costly certification (skin in the game) to inform credit risk. Early research suggests an ever-increasing scope for use of Big Data and incentivized re-intermediation of underwriting. I ask many more questions than current research can answer, hoping to motivate future research.

Managerial Ability and Credit Risk Assessment

Abstract: Research on the credit rating process has primarily focused on how rating agencies incorporate firm characteristics into their rating opinions. We contribute to this literature by examining the impact of managerial ability on the credit rating process. Given debt market participants’ interest in assessing default risk, we begin by documenting that higher managerial ability is associated with lower variability in future earnings and stock returns. We then show that higher managerial ability is associated with higher ratings (i.e., lower assessments of credit risk). To provide more direct identification of the impact of managerial ability, we examine CEO replacements and document that ratings increase (decrease) when CEOs are replaced with more (less) able CEOs. Finally, we show that managerial ability also has capital market implications by documenting that managerial ability is associated with bond offering credit spreads. Collectively, our evidence suggests that managerial ability is an important factor that bond market participants impound into their assessments of firm credit risk.

Procyclicality of Credit Rating Systems: How to Manage it

Abstract: This paper evaluates the characteristics of a Point in Time (PiT) rating approach for the estimation of firms’ credit risk in terms of procyclicality. To this end I first estimate a logit model for the probability default (PD) of a set of Italian non-financial firms during the period 2006-2012, then, in order to address the issue of rating stability (hedging against rating changes) during the financial crisis, I study the effectiveness of ex post smoothing of PDs in terms of obligors’ migration among rating risk grades. As a by-product I further discuss and analyse the role played by the choice of rating scale in producing ratings stability. The results show that ex post PD smoothing is able to remove business cycle effects on the credit risk estimates and to produce a mitigation of obligors’ migration among risk grades over time. The rating scale choice also has a significant impact on rating stability. These findings have important policy implications in banking sector practices in terms of the stability of the financial system.

The Impact of Non-performing Loans on Bank Lending Behavior: Evidence from the Italian Banking Sector

Abstract: The aim of this study is to understand the bank lending behavior during financial crisis, in particular whether an increase of credit risk during this period can lead banks to reduce their lending activity. A second object is to investigate whether cooperative and commercial banks show different behaviors. The analysis is based on a sample of Italian banks (listed and no listed), an example of a country undergoing a credit crunch. The sample consists of 488 listed and unlisted Italian banks observed 2007-2013. Unlisted banks are included because they are the most numerous in the Italian banking system. Findings show a negative impact of credit risk on bank lending behavior, with regard to both credit risk measures: the nonperforming loans and the loan loss provision ratio.

Reduced‐Form versus Structural Models of Credit Risk: A Case Study of Three Models

Abstract: In this paper, we empirically compare two structural models (basic Merton and VasicekKealhofer (VK)) and one reduced-form model (Hull-White (HW)) of credit risk. We propose here that two useful purposes for credit models are default discrimination and relative value analysis. We test the ability of the Merton and VK models to discriminate defaulters from non-defaulters based on default probabilities generated from information in the equity market. We test the ability of the HW model to discriminate defaulters from non-defaulters based on default probabilities generated from information in the bond market. We find the VK and HW models exhibit comparable accuracy ratios on both the full sample and relevant sub-samples and substantially outperform the simple Merton model. We also test the ability of each model to predict spreads in the credit default swap (CDS) market as an indication of each models strength as a relative value analysis tool. We find the VK model tends to do the best across the full sample and relative sub-samples except for cases where an issuer has many bonds in the market. In this case, the HW model tends to do the best. The empirical evidence will assist market participants in determining which model is most useful based on their purpose in hand. On the structural side, a basic Merton model is not good enough; appropriate modifications to the framework make a difference. On the reduced-form side, the quality and quantity of data make a difference; many traded issuers will not be well modeled in this way unless they issue more traded debt. ∗The authors would like to thank Deepak Agrawal, Ittiphan Jearkjirm, Stephen Kealhofer, Hans Mikkelsen, Martha Sellers, Roger Stein, Shisheng Qu, Bin Zeng and seminar participants at MKMV and Prague Fixed Income Workshop (2004) for invaluable suggestions and advice. We are also grateful to Jong Park for providing the code involving the implementation of the Merton model. All remaining errors are ours. Address Correspondence to Dr. Jeffrey R. Bohn, Managing Director, Research Group, Moody’s KMV, 1620 Montgomery Street, San Francisco, CA 94111. E-mail: Jeff.Bohn@mkmv.com Reduced Form vs. Structural Models of Credit Risk: A Case Study of Three Models 1

Trade Credit, Bank Credit, and Flight to Quality: Evidence from French SMEs

Abstract: This paper investigates the impact of the global financial crisis on the allocation of credit to small and medium‐sized enterprises (s). Using samples of rench s from four industries, we found supp...

Comparative credit risk in Islamic and conventional bank

Abstract: In this paper, we consider the levels of credit risk in Islamic and conventional banks. One problem with existing studies is the use of accounting information alone to assess credit risk, and this could be especially misleading with Islamic banking. Using a market-based credit risk measure, Merton's distance-to-default (DD) model, we evaluate the credit risk of 156 conventional banks and 37 Islamic banks across 13 countries between 2000 and 2012. We also calculate the accounting information-based Z-score and nonperforming loan (NPL) ratio for the purpose of comparison. Our results show that Islamic banks have significantly lower credit risk than conventional banks as based on DD. In contrast, and as expected, Islamic banks display much higher credit risk using the Z-score and NPL ratio. These findings suggest that the measure chosen plays a significant role in assessing the actual credit risk of Islamic banks.

Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach

Abstract: Emergence of peer-to-peer lending has opened an appealing option for micro-financing and is growing rapidly as an option in the financial industry. However, peer-to-peer lending possesses a high risk of investment failure due to the lack of expertise on the borrowers' creditworthiness. In addition, information asymmetry, the unsecured nature of loans as well as lack of rigid rules and regulations increase the credit risk in peer-to-peer lending. This paper proposes a credit scoring model using artificial neural networks in classifying peer-to-peer loan applications into default and non-default groups. The results indicate that the neural network-based credit scoring model performs effectively in screening default applications.

Categories, Creditworthiness, and Contagion: How Investors' Shortcuts Affect Sovereign Debt Markets

Abstract: We assess how investors evaluate sovereign borrowers, arguing that sovereign risk is less “sovereign” than previous research assumes. Investors evaluate governments based not only on what they do, but also on investors' views of similar, “peer” countries. Professional investors use investment categorizations (geography, sovereign credit rating, or level of market development) as a heuristic device. As a result, peer country effects, as well as country-specific and global factors (booms, crises, or shocks), should explain sovereign interest rates. The peer effects we expect are regular features of international capital markets, rather than phenomena that occur in periods of market turmoil. We assess our expectations using error correction models of monthly sovereign risk premiums, which reveal significant interdependencies in sovereign risk assessments among countries, net of global and domestic predictors. Such contagion emerges principally in the short term, although we also find robust, long-term ties in sovereign risk assessments among countries sharing common regional classifications. Hence, our evidence suggests that professional investors' reliance on country categorizations facilitates the transmission of market sentiments—which include lower as well as higher risk premiums charged—across groups of countries, even when countries differ in key measures of creditworthiness. Our analyses highlight the importance of investors' ideas regarding country categorizations; they call into question the efficiency of sovereign debt markets.

Banks' Risk Exposures

Abstract: This paper studies U.S. banks' exposure to interest rate and credit risk. We exploit the factor structure in interest rates to represent many bank positions in terms of simple factor portfolios. This approach delivers time varying measures of exposure that are comparable across banks as well as across the business segments of an individual bank. We also propose a strategy to estimate exposure due to interest rate derivatives from regulatory data on notional and fair values together with the history of interest rates. We use the approach to document stylized facts about the recent evolution of bank risk taking.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Risk and Risk Management in the Credit Card Industry

Abstract: Using account level credit-card data from six major commercial banks from January 2009 to December 2013, we apply machine-learning techniques to combined consumer-tradeline, credit-bureau, and macroeconomic variables to predict delinquency In addition to providing accurate measures of loss probabilities and credit risk, our models can also be used to analyze and compare risk management practices and the drivers of delinquency across the banks We find substantial heterogeneity in risk factors, sensitivities, and predictability of delinquency across banks, implying that no single model applies to all six institutions We measure the efficacy of a bank’s risk-management process by the percentage of delinquent accounts that a bank manages effectively, and find that efficacy also varies widely across institutions These results suggest the need for a more customized approached to the supervision and regulation of financial institutions, in which capital ratios, loss reserves, and other parameters are specified individually for each institution according to its credit-risk model exposures and forecasts

The impact of credit risk on profitability performance of commercial banks in Ethiopia

Abstract: The objective of the study was to empirically examine the impact of credit risk on profitability of commercial banks in Ethiopia. For the purpose secondary data collected from 8 sample commercial banks for a 12 year period (2003-2004) were collected from annual reports of respective banks and National Bank of Ethiopia. The data were analyzed using a descriptive statics and panel data regression model and the result showed that credit risk measures: non-performing loan, loan loss provisions and capital adequacy have a significant impact on the profitability of commercial banks in Ethiopia. The study suggested a need for enhancing credit risk management to maintain the prevailing profitability of commercial banks in Ethiopia. Key words: Commercial banks, credit risk, Ethiopia, panel data regression performance, profitability.

Bank Specific and Macroeconomics Dynamic Determinants of Credit Risk in Islamic Banks and Conventional Banks

Abstract: The study analyzes macroeconomic and bank specific determinants of credit risk in Islamic and Conventional Banks. Multivariate Regression analysis is applied on the sample of 15 conventional banks and 13 Islamic Banks in Malaysia over the period between 2000 and 2010. The finding shows that the banks specific determinants of credit risk are uniquely influenced the credit risk formation of Islamic and Conventional banks. The study found that risky sector financing; regulatory capital (REGCAP) and Islamic Contract are significant to credit risk of Islamic banks. For Conventional Banks, loan loss provision, debt-to-total asset ratio, REGCAP, size, earning management and Liquidity are significant factors influencing credit risk. As for macroeconomic factors only Inflation and M3 are significant to credit risk for both Islamic and Conventional banks.