An importance sampling method for portfolios of credit risky assets

The distribution of possible future losses for a portfolio of credit risky corporate assets, such as bonds or loans, shows strongly asymmetric behavior and a fat tail as the consequence of the limited upside of credit (the promised coupon payment) and substantial downside if the corporation defaults. Because of correlation, it is not possible to fully diversify away this fat tail. Detailed correlation models require Monte Carlo simulation to determine the loss distribution for a credit portfolio. This paper describes an importance sampling method that provides substantial speed up for computing economic capital, the rare event quantile of the loss distribution that must be held in reserve by a lending institution for solvency. Tie method, based solely on correlation information, provides accuracy in the tail while maintaining suitable performance for statistics related to the center of the distribution. It is also suitable for long/short portfolios.