学术报告

题      目：Sensitivity Estimates with Computable Bias Bounds

报  告  人：蔡宁  教授  (邀请人：杨舟 )

                                   香港科技大学广州分校

时      间：12月11日  10：00-11：00

地     点：数科院西楼二楼会议室

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 serves as associate editor or editorial board member of several international scholarly journals, including Operations Research, Operations Research Letters,  Stochastic Models, and Digital Finance.

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.

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