Giesecke, Kay, Baeho Kim, Shilin Zhu, "Monte Carlo Algorithms for Default Timing Problems", Management Science, Vol. 57, No. 12, (December 2011), pp. 2115-2129.
Abstract: 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 for performing computations in these models. This paper develops, analyzes, and evaluates two simulation algorithms for intensity-based point process 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.
Keywords: simulation, probability, stochastic model applications, financial institutions, banks.