Credit risk refers to the risk of losses on financial contracts due to a counterparty�s failure to pay its obligations. Such defaults are often associated with bankruptcies; notable recent examples include Enron and K-Mart. In an effort to diversify away this credit risk, banks create portfolios of credit exposures (loans, etc.) to thousands of counterparties. Measuring the amount of risk retained and the probability of large losses on the portfolio (Value at Risk) are challenging modeling problems based on estimating probabilities of rare events such as multiple defaults. Models that capture the complex nature of the default correlations across counterparties generally require Monte Carlo simulation to evaluate.
This talk will focus on several modeling problems associated with credit portfolios and related derivatives such as basket default swaps and collateralized debt obligations (CDOs). These include capturing correlation risk and modeling credit deltas (e.g. sensitivity to default risk or exposure size). The computational challenges associated with the Monte Carlo methods will also be addressed, and an application of importance sampling for the credit portfolio problem will be presented.