南开保险精算大讲堂是南开-泰康保险与精算研究院主办的系列讲座，旨在搭建保险精算领域学术交流平台、推动相关领域的研究与合作。讲座主题涵盖保险精算、风险管理、随机过程、大数据等多个领域，欢迎校内外师生积极参加！

本期南开保险精算大讲堂安排如下：

讲座题目

Conditional value-at-risk under reward-penalty mechanism with applications to robust portfolio management

奖励惩罚机制下的条件风险价值及其在稳健投资组合管理中的应用

主讲人：Jun Cai ‍‍‍

Dr. Jun Cai is a professor in the Department of Statistics and Actuarial Science at the University of Waterloo, Canada. His research interests encompass actuarial science, applied probability, mathematical finance, and operations research. Currently, he focuses on quantitative risk management for insurance and finance, insurance decision problems, dependence modeling, and risk analysis with model uncertainty. His publications have appeared in leading journals such as Operations Research, European Journal of Operational Research, Mathematical Finance, Finance and Stochastics, Journal of Risk and Insurance, Insurance: Mathematics and Economics, ASTIN Bulletin, Scandinavian Actuarial Journal, Advances in Applied Probability, Journal of Multivariate Analysis, and Stochastic Processes and their Applications. Additionally, he and Dr. Tiantian Mao were awarded the 2020 International Actuarial Association (IAA) Bob Alting von Geusau Prize. He also serves as an associate editor for Insurance: Mathematics and Economics.

讲座时间

2025年4月16日（周三）

15：00

讲座地点

金融学院 116 ‍‍‍‍‍‍‍‍‍‍‍‍‍

In this paper, we present robust portfolio selection models by incorporating a reward and penalty mechanism into portfolio management. We assume that the joint distribution of the losses of the underlying risky assets in a portfolio is uncertain but lies within a multivariate distribution set. Our goal is to identify optimal portfolio allocations by minimizing the worst-case conditional value-at-risk (CVaR) of portfolio loss under the reward and penalty mechanism and distribution uncertainty. Our models can also be used to investigate the problem of how to balance portfolio loss and the associated downside risk in portfolio management. The core of such a robust portfolio selection model is the worst-case CVaR. We first derive an explicit closed-form expression for the worst-case CVaR under the reward-penalty mechanism, which generalizes several existing models and results regarding the worst-case CVaR, such as those studied in Jagannathan (1977), Chen et al.(2011), and Cai et al.(2024). We then apply this expression to obtain optimal portfolio allocations that minimize the worst-case CvaR under both a classical mean-covariance-based multivariate distribution set and a generalized mean-covariance-based multivariate distribution set introduced in Kang et al.(2019). Additionally, we utilize real market data to illustrate the application of the proposed models and the corresponding optimal portfolio allocations in portfolio management. Our empirical experiments show that portfolios based on the proposed models have the potential to improve portfolio performance compared to those based on several existing models related to ours. Furthermore, the experiments demonstrate that incorporating downside risk into portfolio loss helps better manage portfolio risk and could achieve higher investment returns than considering either the downside risk or the portfolio loss alone. This talk is based on joint work with Tiantian Mao and Zhiqiao Song.                  

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