Simulating Risk Contributions of Credit Portfolios

Abstract

The 2007–2009 financial turmoil highlighted the need for more active management of credit portfolios. After measuring portfolio credit risk, an important step toward active risk management is to measure risk contributions of individual obligors to the overall risk of the portfolio. In practice, value-at-risk is often used as a risk measure for credit portfolios, and it can be decomposed into a sum of the risk contributions of individual obligors. Estimation of these risk contributions is computationally challenging, mainly because they are expectations conditioned on a rare event. In this paper, we tackle this computational problem by developing a restricted importance sampling (RIS) method for a class of conditional-independence credit risk models, where defaults of obligors are conditionally independent given an appropriately chosen random vector. We propose fast estimators for risk contributions and their confidence intervals. Furthermore, we study the incorporation of traditional importance sampling methods into the RIS method to further improve its efficiency for the widely used Gaussian copula model. Numerical examples show that the proposed method works well.