报告人：Gangnan Yuan（大湾区大学）

时间：2024年06月03日 10：30-

地址：理科楼LA103

摘要：In this talk, we present a novel machine learning approach to estimate the counterparty risk of high-dimensional American options based on modified Gaussian process regression (GPR). We incorporate deep kernel learning and sparse variational Gaussian processes to address the challenges traditionally associated with GPR. These challenges include its diminished reliability in high-dimensional scenarios and the excessive computational costs associated with processing extensive numbers of simulated paths. Our findings indicate that the proposed method surpasses the performance of the least squares Monte Carlo method in high-dimensional scenarios, particularly when the underlying assets are modeled by Merton's jump diffusion model. Moreover, our approach does not exhibit a significant increase in computational time as the number of dimensions grows. Consequently, this method emerges as a potential tool for alleviating the challenges posed by the curse of dimensionality.

简介：Gangnan Yuan is the postdoctoral fellow at the Great Bay University as well as the University of Science and Technology of China. He received his M.Sc degree from the University of Manchester and his Ph.D degree from the University of Macau. His research interests are mainly in machine learning, stochastic differential equations andmathematical finance.

邀请人：吴风艳

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