assistance. The authors acknowledge the help of Edward Altman, Brooks Brady, and Standard and Poors for providing data employed in the paper and its documentation. The authors are grateful to the Institute for Quantitative Investment Research (INQUIRE), UK for its financial support for the project. Acharya is grateful to the Research and Materials Development (RMD) grant from London Business School. Understanding the Recovery Rates on Defaulted Securities We document empirically the determinants of the observed recovery rates on defaulted securities in the United States over the period 1982–1999. The recovery rates are measured using the prices of defaulted securities at the time of default and at the time of emergence from default or from bankruptcy. In addition to seniority and security of the defaulted securities, industry conditions at the time of default are found to be robust and important

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The following article develops a simultaneous multi-factor model for defaults and recoveries. Applying this model, risk parameters can be forecast using systematic and idiosyncratic risk factors and their implied correlations. The theoretical framework is accompanied by an empirical analysis in which a negative correlation between defaults and recoveries over the business cycle is observed. In the study, default and recovery rates are modeled by business cycle indicators and the properties of the economic and regulatory capital given these risk drivers are shown.

This empirical paper addresses the gap between the theoretically well-understood impact of systematic risk on the loss-distribution of a credit-risky loan portfolio and the lack of empirical estimates of the default correlation. To this purpose we start with a one-factor model in which the correlation with the systematic risk factor equals the asset correlation between two firms. The asset correlation is estimated from time series of ten years with default histories of 53280 German companies. The sample is divided into categories that are homogenous with respect to default probability (PD) and firm size. In this way we can explore to what extent correlations depend on these two factors. Several economic explanations why asset correlation depends on size and PD are discussed. The empirical analysis is motivated as well by current proposals for the internal ratings based approach of the new Basel Accord. They suggest that the asset correlation parameter in the risk–weight function depends on the PD and on the firm size of the borrower. Our empirical results are compared with this proposal.

We propose a new framework for modeling systematic risk in Loss-Given-Default (LGD) in the context of credit portfolio losses. The class of models is very flexible and accommodates well skewness and heteroscedastic errors. The quantities in the models have simple economic interpretation. Inference of models in this framework can be unified. Moreover, it allows efficient numerical procedures, such as the normal approximation and the saddlepoint approximation, to calculate the portfolio loss distribution, Value at Risk (VaR) and Expected Shortfall (ES).

This paper studies the effect on economic capital from integrating interest rate and credit spread risk into credit portfolio models. By using fixed forward rates, most credit portfolio models currently employed in the banking industry ignore these risk factors. In contrast to previous studies, this paper accounts for correlated transition risk, credit spread risk, interest rate risk and also recovery rate risk. The simulations show that the error made when neglecting the stochastic nature of interest rates or credit spreads is significant, especially for high quality credit portfolios with low correlations between the obligors’ asset returns.

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UK for its financial support for the project. Acharya is grateful to the Research and Materials Development (RMD) grant from London Business

This paper proposes a theoretical framework to account for systematic risk in recovery and to address the correlation between the firm’s underlying asset process and recovery. Under the proposed framework, the expected value in default under the risk neutral measure can be expressed as a linear function of the expected value under the physical measure. This allows for a simple mapping between expected recovery observed in the data and a measure that can be applied when using risk neutral valuation methods. When calibrating the model to parameters observed in the data, the risk neutral adjustment results in spreads that are 14% higher for a typical bond, and over 30 % higher in some cases. When validating against market data, the evidence suggests that market spreads reflect systematic risk in recovery. We found that approximately 80 % of our sample was estimated with a lower absolute error when

We propose a new framework for modeling systematic risk in Loss-Given-Default (LGD) in the context of credit portfolio losses. The class of models is very flexible and accommodates well skewness and heteroscedastic errors. The quantities in the models have simple economic interpretation. Inference of models in this framework can be unified. Moreover, it allows efficient numerical procedures, such as the normal approximation and the saddlepoint approximation, to calculate the portfolio loss distribution, Value at Risk (VaR) and Expected Shortfall (ES). 1