报告题目：Sensitivity Estimates with Computable Bias Bounds

报告人：蔡宁

时间：2024年4月12日 上午9:00-10:30

地点：西安交通大学创新港涵英楼经济金融研究院8001会议室

报告人简介：

Ning Cai is currently professor and thrust head of the Thrust of Financial Technology at the Hong Kong University of Science and Technology (Guangzhou) (HKUST(GZ)). Previously, he taught at HKUST as assistant professor, associate professor, and professor sequentially. He received both MS and PhD at Columbia University and both BS and MS at Peking University. His research interests include FinTech, financial engineering, applied probability, and stochastic modeling. He is currently serving as Area Editor of Financial Engineering Area of Operations Research Letters and served as Associate Editor of Operations Research from 2015 to 2023.

摘要：

The likelihood ratio method (LRM) is widely used to estimate sensitivities in risk management. Constructions of the LRM estimators depend heavily on the computations of probability density functions (and their derivatives) of the underlying models, which are usually known only through their Laplace transforms under many popular financial models. We propose a Laplace inversion based LRM with computable bias bounds under these models. By selecting the algorithm parameters appropriately, we can obtain LRM estimators with any desired bias level. In addition, some asymptotic properties of our LRM estimators are also investigated. Numerical experiments indicate that our method performs well under a broad range of popular financial models.

经济与金融学院

2024年4月7日