报告1题目：Optimal Bailout for Systemic Risk: A PGO Approach Based on Neural Network
报告人：朱书尚教授
举办时间：2024年9月27日15：00-16：00
举办地点：管理学院A北202
【主讲人简介】
朱书尚，湖南人，本科（1997）和硕士（2000）毕业于湘潭大学，2003年毕业于中国科学院系统科学研究所，获管理学博士学位。2003年7月到2012年1月于复旦大学管理学院任教。2012年1月，以“百人计划”引进人才身份加入中山大学，现任中山大学管理学院财务与投资系教授/博士生导师。多次到香港中文大学、京都大学做访问交流。研究领域包括金融工程、风险管理和运筹学等。当前研究兴趣主要包括投资组合优化、Forward-Looking收益预测、风险值优化、系统性风险传染机制与测度、随机规划等。在国内外专业学术期刊上发表论文70余篇，其中包括在Operations Research, INFORMS Journal on Computing, Mathematical Finance, IEEE Transactions on Automatic Control, IISE Transactions, Journal of Economic Dynamics and Control, Journal of Banking and Finance, Journal of Financial Stability, Quantitative Finance, Journal of Computational Finance,《管理科学学报》和《金融研究》等期刊上发表的多篇论文。兼任中国运筹学会金融工程与金融风险管理分会副理事长、广东经济学会副会长等职。
【主讲内容简介】
In the financial system, the bailout strategy is crucial to cushion the massive loss caused by systemic risk. There is no closed-form formulation of the optimal bailout problem, making solving it difficult. In this paper, we consider the issue of the optimal bailout (capital injection) as a black-box optimization problem, where the black box is characterized as a fixed-point system that adheres to the E-N framework for measuring the systemic risk of the financial system. We propose the so-called“Prediction-Gradient-Optimization”(PGO) framework to address this problem. In this framework, the“Prediction” refers to approximating and predicting the objective function without a closed-form using a neural network, the“Gradient” is calculated based on the previous approximation, and the“Optimization” procedure is further implemented within a gradient projection algorithm to solve the problem. Comprehensive numerical simulations demonstrate that the proposed approach is promising for systemic risk management.
报告2题目：Multi-Period Mean-Variance Portfolio Selection: An MDP Approach
报告人：夏俐教授
举办时间：2024年9月27日16：00-17：00
举办地点：管理学院A北202
【主讲人简介】
中山大学管理学院教授。分别于2002年和2007年在清华大学自动化系获得学士和博士学位，博士生期间在香港科技大学联合培养，博士毕业后分别在IBM中国研究院、沙特国王科技大学从事科研工作，2011年至2019年在清华大学自动化系任教，历任讲师、副教授（博士生导师），2019年调入中山大学。主要研究方向为马氏决策过程、强化学习、排队论、随机博弈等理论研究，以及在能源、金融等领域的应用研究。发表论文100余篇，获得10余项中国和美国发明专利，主持5项国家自然科学基金项目、3项国家重点研发计划子课题、多项华为腾讯公司合作研发项目。担任IEEE Transactions on Automation Science and Engineering、Discrete Event Dynamic Systems等国际权威SCI期刊的副主编（AE）等学术兼职。曾获2021年和2014年教育部高等学校自然科学二等奖等学术奖励。
【主讲内容简介】
Mean-variance optimization is a fundamental problem in portfolio selection, which encounters significant challenges in the scenario of multi-period, caused by the failure of dynamic programming. Li&Ng(2000) and Zhou&Li(2000) developed an embedding method to treat this problem and derived elegant results with closed-form solutions, from the perspective of stochastic control. As Markov decision process (MDP) is a more general methodology for handling stochastic dynamic decision-making, there are also research streams in risk-sensitive MDPs, but it seems that they never merged with the research line of mean-variance portfolio selection from stochastic control. This talk aims to bridge this gap and proposes an MDP approach to handle this classical problem. Our research results show that the risk-sensitive MDP method obtains the exactly same results as stochastic control, and it can further bring a much broader and unified framework.
欢迎有兴趣的教师、博士生、硕士生参加。
