Showing papers on "Credit risk published in 2005"

Quantitative Risk Management: Concepts, Techniques, and Tools

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

The Determinants of Credit Default Swap Premia

Abstract: Using a new dataset of bid and offer quotes for credit default swaps, we investigate the relationship between theoretical determinants of default risk and actual market premia using linear regression. These theoretical determinants are firm leverage, volatility and the riskless interest rate. We find that estimated coefficients for these variables are consistent with theory and that the estimates are highly significant both statistically and economically. The explanatory power of the theoretical variables for levels of default swap premia is approximately 60%. The explanatory power for the differences in the premia is approximately 23%. Volatility and leverage by themselves also have substantial explanatory power for credit default swap premia. A principal component analysis of the residuals and the premia shows that there is only weak evidence for a residual common factor and also suggests that the theoretical variables explain a significant amount of the variation in the data. We therefore conclude that leverage, volatility and the riskfree rate are important determinants of credit default swap premia, as predicted by theory.

Credit Cycles, Credit Risk, and Prudential Regulation

Abstract: This paper finds strong empirical support of a positive, although quite lagged, relationship between rapid credit growth and loan losses. Moreover, it contains empirical evidence of more lenient credit terms during boom periods, both in terms of screening of borrowers and in collateral requirements. Therefore, we confirm the predictions from theoretical models based on disaster myopia, herd behaviour institutional memory and agency problems between banks' managers and shareholders regarding the incentives of the former to engage in too expansionary credit policies during lending booms. The paper also develops a prudential tool, based on loan loss provisions, for banking regulators in order to cope with the former problem.

Explaining Credit Default Swap Spreads with the Equity Volatility and Jump Risks of Individual Firms

Abstract: This paper tries to explain the credit default swap (CDS) premium, using a novel approach to identify the volatility and jump risks of individual firms from high-frequency equity prices. Our empirical results suggest that the volatility risk alone predicts 50 percent of the variation in CDS spread levels, while the jump risk alone forecasts 19 percent. After controlling for credit ratings, macroeconomic conditions, and firms’ balance sheet information, we can explain 77 percent of the total variation. Moreover, the pricing effects of volatility and jump measures vary consistently across investmentgrade and high-yield entities. The estimated nonlinear effects of volatility and jump risks on credit spreads are in line with the implications from a calibrated structural model with stochastic volatility and jumps, although the challenge of simultaneously matching credit spreads and default probabilities remains.

The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications*

Abstract: This paper analyzes the association between default and recovery rates on credit assets and seeks to empirically explain this critical relationship. We examine recovery rates on corporate bond defaults over the period 1982–2002. Our econometric univariate and multivariate models explain a significant portion of the variance in bond recovery rates aggregated across seniority and collateral levels. We find that recovery rates are a function of supply and demand for the securities, with default rates playing a pivotal role. Our results have important implications for credit risk models and for the procyclicality effects of the New Basel Capital Accord.

Asymmetric Information Effects on Loan Spreads

Abstract: This paper estimates the cost arising from information asymmetry between the lead bank and members of the lending syndicate. In a lending syndicate, the lead bank retains only a fraction of the loan but acts as the intermediary between the borrower and the syndicate participants. Theory predicts that private information in the hands of the lead bank will cause syndicate participants to demand a higher interest rate and that a large loan ownership by the lead bank should reduce asymmetric information and the related premium. Nevertheless, the estimated OLS relation between the loan spread and the lead bank's share is positive. This result, however, ignores the fact that we only observe equilibrium outcomes and, therefore, the asymmetric information premium demanded by participants is offset by the diversification premium demanded by the lead bank. Using exogenous shifts in the credit risk of the lead bank's loan portfolio as an instrument, I measure the asymmetric information effect of the lead's share on the loan spread and find that it has a large economic cost, accounting for approximately 4 percent of the total cost of credit.

Modeling Credit Risk for SMEs: Evidence from the US Market

Abstract: Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyze its effectiveness compared to a generic corporate model. The behavior of financial measures for SMEs is analyzed and the most significant variables in predicting the entities' credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on a panel of over 2,000 US firms (with sales less than $65 million) over the period 1994-2002, we develop a one-year default prediction model. This model has an out of sample prediction power which is almost 30% higher than a generic corporate model. An associated objective is to observe our model's ability to lower bank capital requirements considering the new Basel Capital Accord's rules for SMEs.

Corporate financial distress and bankruptcy : predict and avoid bankruptcy, analyze and invest in distressed debt

Abstract: A comprehensive look at the enormous growth and evolution of distressed debt, corporate bankruptcy, and credit risk default This Third Edition of the most authoritative finance book on the topic updates and expands its discussion of corporate distress and bankruptcy, as well as the related markets dealing with high-yield and distressed debt, and offers state-of-the-art analysis and research on the costs of bankruptcy, credit default prediction, the post-emergence period performance of bankrupt firms, and more.

A new fuzzy support vector machine to evaluate credit risk

Abstract: Due to recent financial crises and regulatory concerns, financial intermediaries' credit risk assessment is an area of renewed interest in both the academic world and the business community. In this paper, we propose a new fuzzy support vector machine to discriminate good creditors from bad ones. Because in credit scoring areas we usually cannot label one customer as absolutely good who is sure to repay in time, or absolutely bad who will default certainly, our new fuzzy support vector machine treats every sample as both positive and negative classes, but with different memberships. By this way we expect the new fuzzy support vector machine to have more generalization ability, while preserving the merit of insensitive to outliers, as the fuzzy support vector machine (SVM) proposed in previous papers. We reformulate this kind of two-group classification problem into a quadratic programming problem. Empirical tests on three public datasets show that it can have better discriminatory power than the standard support vector machine and the fuzzy support vector machine if appropriate kernel and membership generation method are chosen.

Credit Risk Transfer and Contagion

Abstract: Some have argued that recent increases in credit risk transfer are desirable because they improve the diversification of risk. Others have suggested that they may be undesirable if they increase the risk of financial crises. Using a model with banking and insurance sectors, we show that credit risk transfer can be beneficial when banks face uniform demand for liquidity. However, when they face idiosyncratic liquidity risk and hedge this risk in an interbank market, credit risk transfer can be detrimental to welfare. It can lead to contagion between the two sectors and increase the risk of crises.

Importance Sampling for Portfolio Credit Risk

Abstract: Monte Carlo simulation is widely used to measure the credit risk in portfolios of loans, corporate bonds, and other instruments subject to possible default. The accurate measurement of credit risk is often a rare-event simulation problem because default probabilities are low for highly rated obligors and because risk management is particularly concerned with rare but significant losses resulting from a large number of defaults. This makes importance sampling (IS) potentially attractive. But the application of IS is complicated by the mechanisms used to model dependence between obligors, and capturing this dependence is essential to a portfolio view of credit risk. This paper provides an IS procedure for the widely used normal copula model of portfolio credit risk. The procedure has two parts: One applies IS conditional on a set of common factors affecting multiple obligors, the other applies IS to the factors themselves. The relative importance of the two parts of the procedure is determined by the strength of the dependence between obligors. We provide both theoretical and numerical support for the method.

The Role of Factoring for Financing Small and Medium Enterprises

Abstract: Around the world, factoring is a growing source of external financing for corporations and small and medium-size enterprises (SMEs). What is unique about factoring is that the credit provided by a lender is explicitly linked to the value of a supplier's accounts receivable and not the supplier's overall creditworthiness. Therefore, factoring allows high-risk suppliers to transfer their credit risk to their high-quality buyers. Factoring may be particularly useful in countries with weak judicial enforcement and imperfect records of upholding seniority claims, because receivables are sold, rather than collateralized, and factored receivables are not part of the estate of a bankrupt SME. Empirical tests find that factoring is larger in countries with greater economic development and growth and developed credit information bureaus. In addition, we find that creditor rights are not related to factoring. This paper also discusses "reverse factoring", which is a technology that can mitigate the problem of borrowers' informational opacity in business environments with weak information infrastructures if only receivables from highquality buyers are factored. We illustrate the case of the Nafin reverse factoring program in Mexico and highlight how the use of electronic channels and a supportive legal and regulatory environment can cut costs and provide greater SME services in emerging markets.

Credit Risk: Pricing, Measurement, and Management

Abstract: Credit Risk: Pricing, Measurement, and Management, by Darrell Duffie and Kenneth J Singleton, 2003, Princeton, NJ: Princeton University Press Credit risk is the major challenge for risk managers and market regulators International regulation of banks' credit risk was put in place in 1988 and since that time there has been no consensus on how to improve that regulatory framework Part of the explanation resides in the complexity of this risk Banks, regulators, and central banks do not agree on how to measure credit risk and, more particularly, on how to compute the optimal capital that is necessary for protecting the different partners that share this risk For example, what proportion of yield spreads on corporate bonds is explained by credit risk? Is it 30 percent, 50 percent, or even 90 percent? Is the credit risk proportion of the observed spreads solely a function of variations in the default probability or is it also explained by variations in the recovery rate over time or across cycles? Are macroeconomic cycles themselves or default risk premia, market liquidity, and even market risk significant determinants of yield spreads? These questions are important because some models such as CreditMetrics use the entire yield spread to compute the capital for credit risk If credit risk explains only a small fraction of yield spreads, these models compute too much capital for regulation and even for credit risk management (Dionne et al, 2004 and references therein) Asking banks to keep too much capital in reserve to cover credit risk can be a source of market distortion in risk management behavior (Allen and Gale, 2003; Dionne and Harchaoui, 2003) For example, it may generate some asset substitution activities that increase the risky position of banks, in order to set the level of risk at its optimal rather than regulatory level All these issues arise in part because credit risk is not well understood So the book by Duffie and Singleton will be welcomed by the academics, regulators, and practitioners who consult it The book has 13 chapters, three appendices (two on affine processes), a comprehensive list of references, and an index (authors and subjects) It covers all subjects related to credit risk It is designed for three broad audiences: academics and graduate students; those involved in the measurement and control of financial risks; and those involved in trading and marketing products with significant credit risk The main focus is modeling credit risk: measuring portfolio credit risk and pricing different securities exposed to credit risk The focus on credit risk management is less important in the book The introduction (indeed the entire book) is very well written and presents the subjects treated with clarity Credit risk is distinguished from other sources of risk such as market risk, liquidity risk, operational risk, systemic risk, and regulatory and legal risk The distinctions take many dimensions such as time horizon, liquidity, the parties implicated, methodology, and information asymmetries However, the authors insist on the fact that this does not mean that all these different risks should be managed separately These different risks may be correlated over time, so integrated frameworks for measuring and pricing them are necessary, particularly for market, credit, and liquidity risks For example, factors underlying changes in credit risk are often correlated with those underlying market risk and changes in liquidity risk can be viewed as a component of market risk and may generate credit risk The last chapter proposes an original way of integrating credit and market risks in a portfolio model The introduction also provides an overview of the book The chapters are organized to highlight the major topics related to credit risk, such as "Definition and Management" (Chapter 2), "Default and Transition" (Chapters 3 and 4), "Valuation" (including valuation of credit derivatives, Chapters 5-9), "Default Correlation" and "Portfolio Valuation" (Chapters 10 and 11), "Credit Risk in OTC Derivatives Positions" and "Portfolio Risk Measurement" (Chapters 12 and 13) …

Privatization and bank performance in developing countries

Abstract: We examine the postprivatization performance of 81 banks from 22 developing countries. Our results suggest that: (i) On average, banks chosen for privatization have a lower economic efficiency, and a lower solvency than banks kept under government ownership. (ii) In the postprivatization period, profitability increases but, depending on the type of owner, efficiency, risk exposure and capitalization may worsen or improve. However, (iii) Over time, privatization yields significant improvements in economic efficiency and credit risk exposure. (iv) We also find that newly privatized banks that are controlled by local industrial groups become more exposed to credit risk and interest rate risk after privatization.

Risk and hedging: Do credit derivatives increase bank risk?

Abstract: The objective of this paper is to investigate whether financial innovation of credit derivatives makes banks more exposed to credit risk. Although credit derivatives are important for hedging and securitizing credit risk – and thereby likely to enhance the sharing of such risk – some commentators have raised concerns that they may destabilize the banking sector. This paper investigates this issue in a simple model driven by costs of financial distress. The analysis identifies two effects of credit derivatives innovation – they enhance risk sharing as suggested by the hedging argument – but they also make further acquisition of risk more attractive. The latter effect, if dominant, can therefore destabilize the banking sector. The critical factor is, perhaps surprisingly, the competitive nature of the existing underlying credit markets. As these markets become more elastic the threat of destabilization increases. The paper discusses issues related to bank regulation within the context of the model.

The role of factoring for financing small and medium enterprises

Abstract: Around the world, factoring is a growing source of external financing for corporations and small and medium-size enterprises (SMEs). What is unique about factoring is that the credit provided by a lender is explicitly linked to the value of a supplier's accounts receivable and not the supplier's overall creditworthiness. Therefore, factoring allows high-risk suppliers to transfer their credit risk to their high-quality buyers. Factoring may be particularly useful in countries with weak judicial enforcement and imperfect records of upholding seniority claims because receivables are sold, rather than collateralized, and factored receivables are not part of the estate of a bankrupt SME. Empirical tests find that factoring is larger in countries with greater economic development and growth and developed credit information bureaus. In addition, the author finds that creditor rights are not related to factoring. The author also discusses reverse factoring, which is a technology that can mitigate the problem of borrowers' informational opacity in business environments with weak information infrastructures if only receivables from high-quality buyers are factored. She illustrates the case of the Nafin reverse factoring program in Mexico and highlights how the use of electronic channels and a supportive legal and regulatory environment can cut costs and provide greater SME services in emerging markets.

Securitization of Life Insurance Assets and Liabilities

Abstract: INTRODUCTION Securitization is one of the most important innovations of modern finance. The securitization process involves the isolation of a pool of assets or rights to a set of cash flows and the repackaging of the asset or cash flows into securities that are traded in capital markets. The trading of cash flow streams enables the parties to the contract to manage and diversify risk, to take advantage of arbitrage opportunities, or to invest in new classes of risk that enhance market efficiency. The cash flow streams to be traded often involve contingent payments as well as more predictable components which may be subject to credit and other types of counterparty risk. Securitization provides a mechanism whereby contingent and predictable cash flow streams arising out of a transaction can be unbundled and traded as separate financial instruments that appeal to different classes of investors. In addition to facilitating risk management, securitization transactions also add to the liquidity of financial markets, replacing previously untraded on-balance-sheet assets and liabilities with tradeable financial instruments. The securitization era began in the 1970s with the securitization of mortgage loans by the government sponsored enterprises (GSEs) Fannie Mae, Ginnie Mae, and Freddie Mac, which were created by the federal government with the objective of facilitating home ownership by providing a reliable supply of home mortgage financing. The securitization process enabled mortgage originators such as banks, thrift institutions, and insurers to move mortgage loans off their balance sheets, freeing up funds for additional lending. In the process, a new class of highly rated, liquid securities was created, enhancing portfolio opportunities for investors. The next major development in securitization was the introduction of asset-backed securities (ABS) based on other types of assets. This market began in 1985 with the securitization of approximately $1 billion in automobile loans and later expanded to include credit card receivables, home equity loans, aircraft-backed loans, student loans, and numerous other asset classes. In 2003, new issue volume of mortgage-backed and nonmortgage-backed ABS reached $2.1 trillion and $585 billion, respectively. (1) Although the insurance industry in the United States accounts for approximately $4 trillion in assets with corresponding liabilities and equity capital that would seem to be candidates for securitization, securitization has been relatively slow to catch on in this industry. The first U.S. insurance securitizations took place in 1988 and involved sales of rights to emerging profits from blocks of life insurance policies and annuities (Millette et al., 2002). Insurance linked securitizations accelerated during the 1990s with the development of catastrophic risk (CAT) bonds and options and a growing volume of life insurance and annuity securitizations. However, the volume of insurance transactions remains small in comparison with other types of ABS. Securitization has the potential to improve market efficiency and capital utilization in the insurance industry, enabling insurers to compete more effectively with other financial institutions. Through securitization insurers can reduce their cost of capital, increase return on equity, and improve other measures of operating performance. Securitization offers insurers the opportunity to unlock the embedded profits in blocks of insurance presently carried on balance sheet and to provide an alternative source of financing in an industry where traditional financing mechanisms are often restricted due to regulation. Securitized transactions also permit insurers to achieve liquidity goals and can add transparency to many on-balance-sheet assets and liabilities traditionally characterized by illiquidity, complexity, and informational opacity. Securitization also offers new sources of risk capital to hedge against underwriting risk more efficiently than traditional techniques such as reinsurance and letters of credit. …

Comparing possible proxies of corporate bond liquidity

Abstract: We consider nine different proxies (issued amount, listed, euro, on-the-run, age, missing prices, yield volatility, number of contributors and yield dispersion) to measure corporate bond liquidity and use a four-variable model to control for interest rate risk, credit risk, maturity and rating differences between bonds. The null hypothesis that liquidity risk is not priced in our data set of euro corporate bonds is rejected for eight out of nine liquidity proxies. We find significant liquidity premia, ranging from 13 to 23 basis points. A comparison test between liquidity proxies shows limited differences between the proxies.

A Multivariate Jump-Driven Financial Asset Model

Abstract: In this paper, we propose a multivariate model for nancial assets which incorporates jumps, skewness, kurtosis and stochastic volatility, and discuss its applications in the context of equity and credit risk. In the former case we describe the stochastic behavior of a series of stocks or indexes, in the latter we apply the model in a multi- rm, value-based default model. Starting from a independent Brownian world, we will introduce jumps and other deviations from normality, as well as non-Gaussian dependence, by the simple but very strong technique of stochastic time-changing. We work out the details in the case of a Gamma time-change, thus obtaining a multivariate Variance Gamma (VG) setting. We are able to characterize the model from an analytical point of view, by writing down the joint distribution function of the assets at any point in time and by studying their association via the copula technique. The model is also computationally friendly, since numerical results require a modest amount of time and the number of parameters grows linearly with the number of assets. The main feature of the model however is the fact that - opposite to other, non jointly Gaussian settings - its risk neutral dependence can be calibrated from univariate derivative prices. Examples from the equity and credit market show the goodness of fit attained.

Business and default cycles for credit risk

Abstract: Various economic theories are available to explain the existence ofcredit and default cycles. There remains empirical ambiguity, how-ever, as to whether these cycles coincide. Recent papers suggest bytheir empirical research set-up that they do, or at least that defaultsand credit spreads tend to co-move with macro-economic variables. Iftrue, this is important for credit risk management as well as for regu-lation and systemic risk management. In this paper, we use 1933–1997U.S. data on real GDP, credit spreads, and business failure rates toshed new light on the empirical evidence. We use a multivariate un-observed components framework to disentangle credit and businesscycles. We distinguish two types of cycles in the data, correspond-ing to periods of around 6 and 11-16 years, respectively. Cyclicalco-movements between GDP and business failures mainly arise at thelonger frequency. At the higher frequency of 6 years, co-cyclicalityis less clear-cut. We also show that spreads reveal a positive andnegative co-cyclicality with failure rates and GDP, respectively. Thispattern disappears, however, if we concentrate on the post World WarII period. We comment on the implications of our findings for creditrisk management.Key words: credit cycles; business cycles; defaults; credit risk; pro-cyclicality; multivariate unobserved component models.JEL Codes: C19; G21.

The relationship between default prediction and lending profits: Integrating ROC analysis and loan pricing

Abstract: In evaluating credit risk models, it is common to use metrics such as power curves and their associated statistics. However, power curves are not necessarily easily linked intuitively to common lending practices. Bankers often request a specific rule for defining a cut-off above which credit will be granted and below which it will be denied. In this paper we provide some quantitative insight into how such cut-offs can be developed. This framework accommodates real-world complications (e.g., “relationship” clients). We show that the simple cut-off approach can be extended to a more complete pricing approach that is more flexible and more profitable. We demonstrate that in general more powerful models are more profitable than weaker ones and we provide a simulation example. We also report results of another study that conservatively concludes a mid-sized bank might generate additional profits on the order of about $4.8 million per year after adopting a moderately more powerful model.

Merton's model, credit risk and volatility skews

Abstract: In 1974 Robert Merton proposed a model for assessing the credit risk of a company by characterizing the company’s equity as a call option on its assets. In this paper we propose a method for estimating the model’s parameters from the implied volatilities of options on the company’s equity. We use data from the credit default swap market to compare our implementation of Merton’s model with the traditional approach to implementation.

Merton's Model, Credit Risk and Volatility Skews

Abstract: In 1974 Robert Merton proposed a model for assessing the credit risk of a company by characterizing the company's equity as a call option on its assets. In this paper we propose a method for estimating the model's parameters from the implied volatilities of options on the company's equity. We use data from the credit default swap market to compare our implementation of Merton's model with the traditional approach to implementation.

A survey of the issues in consumer credit modelling research

Abstract: Methods for assessing the credit risk when lending to consumers has been in operation for 50 years. Yet, there are probably now more opportunities and challenges for research into the development of this area than ever before. This paper surveys the development of the methodology, describes the current environment for consumer lending and seeks to identify some of the modelling areas and issues that are actively being researched or should be.

Bond Prices, Default Probabilities and Risk Premiums

Abstract: A feature of credit markets is the large difference between probabilities of default calculated from historical data and probabilities of default implied from bond prices (or from credit default swaps). This paper illustrates and discusses the reasons for the difference between historical and risk neutral probabilities.