Monte Carlo Algorithms for Default Timing Problems

• 成果类型: Article
• 署名作者: Giesecke, Kay; Kim, Baeho; Zhu, Shilin
• 署名单位: Stanford University; Korea University; Stanford University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.1110.1411
• 发表日期: 2011
• 页码: 2115-2129
• 关键词: simulation; probability; stochastic model applications; Financial institutions; banks

摘要

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.