Systemic Credit Risk Research Articles

Pricing CDOs with state-dependent stochastic recovery rates

Up to the 2007 crisis, research within bottom-up CDO models mainly concentrated on the dependence between defaults. Since then, due to substantial increases in market prices of systemic credit risk protection, more attention has been paid to recovery rate assumptions. In this paper, we use stochastic orders theory to assess the impact of recovery on CDOs and show that, in a factor copula framework, a decrease of recovery rates leads to an increase of the expected loss on senior tranches, even though the expected loss on the portfolio is kept fixed. This result applies to a wide range of latent factor models and is not specific to the Gaussian copula model. We then suggest introducing stochastic recovery rates in such a way that the conditional on the factor expected loss (or, equivalently, the large portfolio approximation) is the same as in the recovery markdown case. However, granular portfolios behave differently. We show that a markdown is associated with riskier portfolios than when using the stochastic recovery rate framework. As a consequence, the expected loss on a senior tranche is larger in the former case, whatever the attachment point. We also deal with implementation and numerical issues related to the pricing of CDOs within the stochastic recovery rate framework. Due to differences across names regarding the conditional (on the factor) losses given default, the standard recursion approach becomes problematic. We suggest approximating the conditional on the factor loss distributions, through expansions around some base distribution. Finally, we show that the independence and comonotonic cases provide some easy to compute bounds on expected losses of senior or equity tranches.

Aggregating Credit and Market Risk: The Impact of Model Specification

We investigate the effect of model specification on the aggregation of (correlated) market and credit risk. We focus on the functional form linking systematic credit risk drivers to default probabilities. Examples include the normal based probit link function for typical structural models, or the exponential (Poisson) link function for typical reduced form models. We first show analytically how model specification impacts 'diversification benefits' for aggregated market and credit risk. The specification effect can lead to Value-at-Risk (VaR) reductions in the range of 3 percent to 47 percent, particularly at high confidence level VaRs. We also illustrate the effects using a fully calibrated empirical model for US data. The empirical effects corroborate our analytic results.

Mycoses invasives chez les greffés rénaux : à propos de huit cas

This study proposes a novel framework which combines marginal probabilities of default estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology and the generalized dynamic factor model (GDFM) supplemented by a dynamic t-copula. The framework models banks’ default dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures banking systemic credit risk in the three forms categorized by the European Central Bank: (1) credit risk common to all banks; (2) credit risk in the banking system conditional on distress on a specific bank or combinations of banks; and (3) the buildup of banking system vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the banking sector short-term and conditional forward default measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. Finally, the framework produces robust out-of-sample forecasts of the banking systemic credit risk measures. This paper advances the agenda of making macroprudential policy operational.

Banking Systemic Vulnerabilities: A Tail-Risk Dynamic CIMDO Approach

This study proposes a novel framework which combines marginal probabilities of default estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology of Segoviano, and the generalized dynamic factor model (GDFM) supplemented by a dynamic t-copula. The framework models banks? default dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures banking systemic credit risk in three forms: (1) credit risk common to all banks; (2) credit risk in the banking system conditional on distress on a specific bank or combinations of banks and; (3) the buildup of banking system vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the banking sector short-term and conditional forward default measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. Finally, the framework produces robust outof-sample forecasts of the banking systemic credit risk measures. This paper advances the agenda of making macroprudential policy operational.

Systemic Capital Requirements

The credit risk that an individual bank poses to the rest of the financial system depends on its size, the type of exposures it has to the real economy, and its obligations to other institutions. This paper describes a system-wide risk management approach to calibrating individual banks’ capital requirements that takes into account these factors and which correspond to a policymaker’s chosen target for systemic credit risk. The optimization strategy identifies the minimum level of aggregate capital for the system and its distribution across banks that are consistent with a chosen objective for systemic credit risk. This parameterizes a trade-off between efficiency and stability.

Pricing CDOs with State Dependent Stochastic Recovery Rates

Up to the 2007 crisis, research within bottom‐up CDO models mainly concentrated on the dependence between defaults. However, due to the substantial increase in the market price of systemic credit risk protection, more attention has been paid to recovery rate assumptions.In this paper, we focus first on deterministic recovery rates in a factor copula framework. We use stochastic orders theory to assess the impact of a recovery markdown on CDOs and show that it leads to an increase of the expected loss on senior tranches, even though the expected loss on the portfolio is kept fixed. This result applies to a wide range of latent factor models.We then suggest introducing stochastic recovery rates in such a way that the conditional on the factor expected loss (or equivalently the large portfolio approximation) is the same as in the recovery markdown case. However, granular portfolios behave differently. We show that a markdown is associated with riskier portfolios that when using the stochastic recovery rate framework. As a consequence, the expected loss on a senior tranche is larger in the former case, whatever the attachment point.We also deal with implementation and numerical issues related to the pricing of CDOs within the stochastic recovery rate framework. Due to differences across names regarding the conditional (on the factor) losses given default, the standard recursion approach becomes problematic. We suggest approximating the conditional on the factor loss distributions, through expansions around some base distribution.Finally, we show that the independence and comonotonic cases provide some easy to compute bounds on expected losses of senior or equity tranches.

The robustness of simulation-based Markovian transition probabilities for ultra-small samples of non-performing credit

The analysis of systemic credit risk by financial regulators is largely affected by the paucity of data. Supervisors receive reports on proportions of performing and non-performing aggregate loan classes of individual banks—corporate, mortgage and consumer—often with very short history. The transition to different proportions of aggregate credit qualities can be seen as a Markov process and Christodoulakis [J. Credit Risk, 2007, 3(3), 25–39] proposed a simulation-based method for the estimation of transition probabilities under non-negativity constraints, based on the work of Kloek and van Dijk [Econometrica, 1978, 46(1), 1–19] and van Dijk and Kloek [J. Econometrics, 1980, 14, 307–328]. This paper provides Monte Carlo robustness checks for the performance of this estimator in comparison to Least Squares and Restricted Least Squares (OLS-R) in the presence of ultra-small samples. When true transition probabilities are very large or very small, the least squares estimators severely violate non-negativity restrictions, leading to a spuriously small bias as compared to Monte Carlo integration (MCI). This result is intensified for low volatility regimes and small samples. The bias of MCI diminishes in higher volatility regimes and larger samples, irrespective of the number of Bayesian replications. Regarding the accuracy statistics, we observe that the variance of every estimator increases in smaller samples and lower volatility regimes. MCI is revealed as more accurate or at least equivalent to OLS-R in all cases. Our empirical applications with real US and European data on aggregate credit portfolios revealed significant violations of non-negativity constraints by least squares methods, which contribute to favourable conclusions for MCI.

Linking global economic dynamics to a South African-specific credit risk correlation model

In order to address practical questions in credit portfolio management it is necessary to link the cyclical or systematic components of firm credit risk with the firm's own idiosyncratic credit risk as well as the systematic credit risk component of every other exposure in the portfolio. This paper builds on the methodology proposed by Pesaran, Schuermann, and Weiner [Pesaran, M.H., Schuermann, T., and Weiner, S.M., (2004), Modeling regional interdependencies using a global error correcting macroeconometric model, Journal of Business and Economic Statistics, 22, 2, 129–169.] and supplemented by Pesaran, Schuermann, Treutler and Weiner [Pesaran, M.H., Schuermann, T., Treutler, B., and Weiner, S.M., (2006), Macroeconomic dynamics and credit risk: a global perspective, Journal of Money, Credit, and Banking, Volume 38, Number 5, August 2006, 1211–1261.] which has made a significant advance in credit risk modelling in that it avoids the use of proprietary balance sheet and distance-to-default data, focusing on credit ratings which are more freely available. In this paper a country-specific macroeconometric risk-driver engine which is compatible with and could feed into the GVAR model and framework of PSW (2004) is constructed, using vector error-correcting (VECM) techniques. This allows conditional loss estimation of a South African-specific credit portfolio but also opens the door for credit portfolio modelling on a global scale, as such a model can easily be linked to the GVAR model. The set of domestic factors is extended beyond those used in PSW (2004) in such a way that the risk-driver model is applicable for both retail and corporate credit risk. As such, the model can be applied to a total bank balance sheet, incorporating the correlation and diversification between both retail and corporate credit exposures. Assuming statistical over-identification restrictions, the results indicate that it is possible to construct a South African component for the GVAR model that can easily be integrated into the global component. From a practical application perspective the framework and model is particularly appealing since it can be used as a theoretically consistent correlation model within a South African-specific credit portfolio management tool.

Systemic Credit Risk, Contagion, and the Pricing of Distressed Illiquid Securities

The credit default swap index, the most credit-sensitive security, can provide an early warning of broader future credit losses. Credit markets also provide useful information for understanding how contagion spreads from market to market and can help forecast future excess stock returns. Finally, the current situation in the credit markets has provided an opportunity to better understand how changes in liquidity affect pricing.

On the Pricing of Credit Risk in Eurocurrency Market

Most of previous studies on credit risk have been focused on corporate bond markets. This paper focuses on whether the credit risk is priced in the Eurocurrency market. The empirical test relies on a multivariate GARCH (1,1)-in-mean version of the ICAPM model to describe the joint stochastic process of three state variables: world market risk, interest rate risk, and credit risk, during the period January 1986 to December 2002. The paper documents a negative, significant, and time-varying systematic credit risk premium.

Systemic Credit Risk: What Is the Market Telling Us?

The ongoing subprime crisis raises many concerns about the possibility of even more widespread credit shocks. We describe a simple linear version of a sophisticated model that can be used to extract information about macroeconomic credit risk from the prices of tranches of liquid credit indices. The market appears to price three types of credit risk: idiosyncratic risk at the level of individual companies, sectorwide risk at the level of companies within an industry, and economywide or systemic risk. We applied the model to the recent behavior of tranches in the U.S. and European credit derivatives markets and show that the current crisis has more than twice the systemic risk of the automotive-downgrade credit crisis of May 2005.

A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk

We model 1981–2005 quarterly default frequencies for a panel of U.S. firms in different rating and age classes from the Standard and Poor database. The data are decomposed into systematic and firm-specific risk components, where the systematic component reflects the general economic conditions and the default climate. We need to cope with: the shared exposure of each age cohort, industry, and rating class to the same systematic risk factor; strongly non-Gaussian features of the individual time series; possible dynamics of an unobserved common risk factor; changing default probabilities over the age of the rating; and missing observations. We propose a non-Gaussian multivariate state-space model that deals with all of these issues simultaneously. The model is estimated using importance sampling techniques that have been modified to a multivariate setting. We show in a simulation study that such a multivariate approach improves the performance of the importance sampler. In our empirical work, we find that systematic credit risk may differ substantially in terms of magnitude and timing across industries.

Credit cycles and macro fundamentals

Abstract We use an intensity-based framework to study the relation between macroeconomic fundamentals and cycles in defaults and rating activity. Using Standard and Poor's U.S. corporate rating transition and default data over the period 1980–2005, we directly estimate the default and rating cycle from micro data. We relate this cycle to the business cycle, bank lending conditions, and financial market variables. In line with earlier studies, the macro variables appear to explain part of the default cycle. However, we strongly reject the correct dynamic specification of these models. The problem is solved by adding an unobserved dynamic component to the model, which can be interpreted as an omitted systematic credit risk factor. By accounting for this latent factor, many of the observed macro variables loose their significance. There are a few exceptions, but the economic impact of the observed macro variables for credit risk remains low. We also show that systematic credit risk factors differ over transition types, with risk factors for downgrades being noticeably different from those for upgrades. We conclude that portfolio credit risk models based only on observable systematic risk factors omit one of the strongest determinants of credit risk at the portfolio level. This has obvious consequences for current modeling and risk management practices.

Systemic Credit Risk: What Is the Market Telling Us?

The ongoing subprime crisis raises many concerns about the possibility of even more widespread credit shocks. We describe a simple linear version of a sophisticated model that can be used to extract information about macroeconomic credit risk from the prices of tranches of liquid credit indices. The market appears to price three types of credit risk: idiosyncratic risk at the level of individual companies, sectorwide risk at the level of companies within an industry, and economywide or systemic risk. We applied the model to the recent behavior of tranches in the U.S. and European credit derivatives markets and show that the current crisis has more than twice the systemic risk of the automotive-downgrade credit crisis of May 2005.

Analytical methods for hedging systematic credit risk with linear factor portfolios

Multi-factor credit portfolio models are used widely today for managing economic capital and pricing collateralized debt obligations (CDOs) and asset-backed securities. Commonly, practitioners allocate capital to the portfolio components (sub-portfolios, counterparties, or transactions). The hedging of credit risk is generally also focused on the ‘deltas’ of underlying names. We present analytical results for hedging portfolio credit risk with linear combinations of systematic factors, based on the minimization of systematic variance of portfolio losses. We solve these problems within a multi-factor Merton-type credit portfolio model, and apply them to hedge systematic credit default losses of loan portfolios and CDOs.

Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach

Abstract We use Markov Chain Monte Carlo (MCMC) methods for the parameter estimation and the testing of conditional asset pricing models. In contrast to traditional approaches, it is truly conditional because the assumption that time variation in betas is driven by a set of conditioning variables is not necessary. Moreover, the approach has exact finite sample properties and accounts for errors‐in‐variables. Using S&P 500 panel data, we analyse the empirical performance of the CAPM and the Fama and French (1993) three‐factor model. We find that time‐variation of betas in the CAPM and the time variation of the coefficients for the size factor (SMB) and the distress factor (HML) in the three‐factor model improve the empirical performance. Therefore, our findings are consistent with time variation of firm‐specific exposure to market risk, systematic credit risk and systematic size effects. However, a Bayesian model comparison trading off goodness of fit and model complexity indicates that the conditional CAPM performs best, followed by the conditional three‐factor model, the unconditional CAPM, and the unconditional three‐factor model.

Local Government Borrowing and Repayment in Indonesia: Does Fiscal Capacity Matter?

Most regional government borrowing in Indonesia has been conducted via central government mechanisms. While the terms and conditions of central lending have been highly favorable to regional borrowers, repayment of debt has been very poor. The empirical evidence suggests that local governments have borrowed well within their fiscal capacities to repay and that repayment problems are largely a function of unwillingness to repay debts and of central government acquiescence. Regional borrowing must significantly increase in the years ahead in order to meet growing demand for infrastructure services and lending to regions will have to become more market-based. Reducing systemic credit risk and improving the creditworthiness of regional borrowers are prerequisites to increased and sustained market-based lending.

Insuring banks against systematic credit risk

Insuring banks against systematic credit risk