时 间：2024年11月01日15:00 - 16:00
报告人：韦晓中央财经大学副教授
地 点：普陀校区理科大楼A1514
主持人：钱林义华东师范大学教授
摘 要：
Based on the Monte Carlo (MC), Machine Learning and control variate (CV) techniques as discussed in the recent literature for computing the price of American Arithmetic option under a high dimension Black-Scholes dynamics, we enhance price accuracy by selecting optimal control variates and their coefficients, thereby reducing the variance of the estimator. For the first two cases, we use the price of European Arithmetic option and American Geometric option as a single control variate respectively, then combine both of them as multiple control variates in the third case. Numerical results show that employing the appropriate coefficients can further reduce the variance for American option pricing to between 30% and 70%. Furthermore, by employing American Geometric options as control variate, we can further reduce the variance to around 20% of the original even when the underlying asset dimension reaches 40, owing to the more similar structure between American Geometric options and American Arithmetic options. This is a joint work with Ludovic GOUDENEGE （University of Paris-Saclay） and Yahui ZHANG （CUFE & University of Paris-Saclay）
报告人简介：
中央财经大学保险学院、中国精算研究院副教授。武汉大学理学博士，法国国家信息与自动化研究院（INRIA）金融数学项目组（Mathfi Team）博士后，该项目组的金融软件Premia研发设计的permanent contributor。在曾先后访问香港科技大学，加拿大滑铁卢大学，意大利乌迪内大学，进行合作研究；曾在国际四大精算期刊《Insurance: Mathematics and Economics》《ASTIN Bulletin》《Scandinavian Actuarial Journal》《North American Actuarial Journal》及数学、概率统计期刊《中国科学：数学》《Statistics & Probability Letters》《Journal of Applied Probabilities》等发表多篇学术论文；主持国家自然科学基金项目、教育部人文社科项目等多项课题。