一、题目：Discrete-time Variance-optimal Deep Hedging in Affine GARCH Models

二、主讲人：

刘彦初，现就职于中山大学岭南学院，担任副院长，金融学副教授，博士生导师。香港中文大学金融工程学博士、博士后，中国科学技术大学理学硕士与理学学士。主要研究兴趣为金融工程，金融科技，以及相关应用。在《管理科学学报》，《Operations Research》，《INFORMS Journal on Computing》，《Journal of Economic Dynamics and Control》，《European Journal of Operational Research》，《Quantitative Finance》，《Journal of Futures Markets》，《Insurance: Mathematics and Economics》，《IEEE Transactions on Engineering Management》，《European Journal of Finance》，《Annals of Operations Research》，《Finance Research Letters》等国内外主流学术期刊上发表（含接收）论文近40篇。主持国家自然科学基金面上和青年项目、广州期货交易所首批对外合作课题、中国期货业协会研究课题等科研项目。

三、时间：2022年11月2日 星期三上午，10:00-11:30

四、地点：腾讯会议287-174-259

五、主持人： 姜富伟教授，金融工程系主任

六、内容简介

Variance-optimal hedging in a discrete-time framework is a practical option-hedging strategy that aims to reduce the residual risk. It has been widely used in volatility trading desks. In this paper, we solve the variance-optimal hedging problem for affine GARCH models both semi-explicitly and through deep learning. Applying the Laplace transform method, we derive semi-explicit formulas for the variance-optimal hedging strategy and initial endowment. We also apply the Long Short-Term Memory (LSTM) recurrent neural network (RNN) architectures and solve for optimal hedging strategies under mean square error loss function with transaction costs. Numerical examples illustrate the hedging performance for different approaches, option styles, hedging frequencies and transaction costs. [This is a joint work with Hongkai Cao, Zhenyu Cui and Ying Yu.]