Efficient estimation of default correlation for multivariate jump-diffusion processes

Abstract

Evaluation of default correlation is an important task in credit risk analysis. In many practical situations, it concerns the joint defaults of several correlated firms, the task that is reducible to a first passage time (FPT) problem. This task represents a great challenge for jump-diffusion processes (JDP), where except for very basic cases, there are no analytical solutions for such problems. In this contribution, we generalize our previous fast Monte-Carlo method (non-correlated jump-diffusion cases) for multivariate (and correlated) jump-diffusion processes. This generalization allows us, among other things, to evaluate the default events of several correlated assets based on a set of empirical data. The developed technique is an efficient tool for a number of other applications, including credit risk and option pricing.