Monte Carlo Algorithms for Default Timing Problems
针对信用资产组合中的相关违约风险，开发并评估了两种蒙特卡洛模拟算法，扩展了传统细化方案以处理无界事件强度，适用于自上而下和自下而上的违约风险模型。
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. This paper was accepted by Assaf Zeevi, stochastic models and simulation.