Reducing Bias in Event Time Simulations via Measure Changes

• 成果类型: Article
• 署名作者: Giesecke, Kay; Shkolnik, Alexander
• 署名单位: Stanford University; University of California System; University of California Santa Barbara
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X
• DOI: 10.1287/moor.2021.1156
• 发表日期: 2022
• 页码: 969-988
• 关键词: jump; CONVERGENCE; defaults; RISK

摘要

Stochastic point process models of event timing are common in many areas, including finance, insurance, and reliability. Monte Carlo simulation is often used to perform computations for these models. The standard sampling algorithm, which is based on a time-change argument, is widely applicable but generates biased simulation estimators. This article develops and analyzes a change of probability measure that can reduce or even eliminate the bias without restricting the scope of the algorithm. A result of independent interest offers new conditions that guarantee the existence of a broad class of point process martingales inducing changes ofmeasure. Numerical results illustrate our approach.