时 间：2024年11月01日 14:00 - 15:00
报告人：蔡军加拿大滑铁卢大学教授
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：
The expected regret and target semi-variance are two of the most important risk measures for downside risk. When the distribution of a loss is uncertain, and only partial information of the loss is known, their worst-case values play important roles in robust risk management for finance, insurance, and many other fields. Jagannathan (1977) derived the worst-case expected regrets when only the mean and variance of a loss are known and the loss is arbitrary, symmetric, or non-negative. While Chen et al. (2011) obtained the worst-case target semi-variances under similar conditions but focusing on arbitrary losses. In this paper, we first complement the study of Chen et al. (2011) on the worst-case target semi-variances and derive the closed-form expressions for the worst-case target semi-variance when only the mean and variance of a loss are known, and the loss is symmetric or non-negative. Then, we investigate worst-case target semi-variances over uncertainty sets that represent undesirable scenarios faced by an investor. Our methods for deriving these worst-case values are different from those used in Jagannathan (1977) and Chen et al. (2011). As applications of the results derived in this paper, we propose robust portfolio selection methods that minimize the worst-case target semi-variance of a portfolio loss over different uncertainty sets. To explore the insights of our robust portfolio selection methods, we conduct numerical experiments with real financial data and compare our portfolio selection methods with several existing portfolio selection models related to the models proposed in this paper. This talk is based on joint work with Zhanyi Jiao and Tiantian Mao.
报告人简介：
Dr. Jun Cai is a professor in the Department of Statistics and Actuarial Science at the University of Waterloo, Canada. His research interests include 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.