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

二、主講人：

劉彥初，現就職于中山大學嶺南學院，擔任副院長，金融學副教授，博士生導師。香港中文大學金融工程學博士、博士後，中國科學技術大學理學碩士與理學學士。主要研究興趣為金融工程，金融科技，以及相關應用。在《管理科學學報》，《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》等國内外主流學術期刊上發表（含接收）論文近40篇。主持國家自然科學基金面上和青年項目、廣州期貨交易所首批對外合作課題、中國期貨業協會研究課題等科研項目。

三、時間：2022年11月2日 星期三上午，10:00-11:30

四、地點：騰訊會議287-174-259

五、主持人： 姜富偉教授，金融工程系主任

六、内容簡介

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.]