Monte Carlo Algorithms for Default Timing Problems

Dynamic, intensity based point process models are widely used to measure and price the correlated default risk in portfolios of credit-sensitive assets such as loans and corporate bonds. Monte Carlo simulation is an important tool to perform computations in these models. This paper develops, analyzes and evaluates two simulation algorithms for these models. The algorithms extend the conventional thinning scheme to the case where the event intensity is unbounded, a feature common to many standard model formulations. Numerical results illustrate the performance of the algorithms for a familiar top-down model and a novel bottom-up model of correlated default risk