报告题目1：Distributionally robust reinsurance with Value-at-Risk and Conditional Value-at-Risk
毛甜甜现为中国科学技术大学管理学院副教授。2012年博士毕业于中国科学技术大学，主要研究领域为：风险度量，极值理论和随机比较。目前已经在Mathematical Programming，Finance and Stochastics，SIAM Journal on Financial Mathematics，Extremes等期刊发表论文三十余篇。
A basic assumption of the classic reinsurance model is that the distribution of the loss is precisely known. In practice, only partial information is available for the loss distribution due to the lack of data and estimation error. We study a distributionally robust reinsurance problem by minimizing the maximum Value-at-Risk (or the worst-case VaR) of the total retained loss of the insurer for all loss distributions with known mean and variance. Our model handles typical stop-loss reinsurance contracts. We show that a three-point distribution achieves the worst-case VaR of he total retained loss of the insurer, from which the closed-form solutions of the worst-case distribution and optimal deductible are obtained. Moreover, we show that the worst-case Conditional Value-at-Risk of the total retained loss of the insurer is equal to the worst-case VaR, and thus the optimal deductible is the same in both cases.
报告题目2：E-values and e-backtesting risk measures
报告人：Ruodu Wang(University of Waterloo)
Dr. Ruodu Wang is University Research Chair and Associate Professor of Actuarial Science and Quantitative Finance at the University of Waterloo in Canada. He received his PhD in Mathematics (2012) from the Georgia Institute of Technology, after completing his Bachelor (2006) and Master’s (2009) degrees at Peking University. He holds editorial positions in leading journals in actuarial science and mathematical economics, including Co-Editor of the European Actuarial Journal, and Co-Editor of ASTIN Bulletin. His scientific work has appeared in academic journals in various other fields, such as Management Science, Operations Research, The Annals of Statistics, Biometrika, The Annals of Applied Probability, Mathematics of Operations Research, Mathematical Finance, and Finance & Stochastics. He is an affiliated member of RiskLab at ETH Zurich. He received the Golden Jubilee Research Excellence Award from the Faculty of Mathematics at Waterloo in 2017 and a Discovery Accelerator Supplement Award from the Natural Sciences and Engineering Research Council in 2018.
Testing statistical hypothesis is usually done in sciences using p-values. Recently, e-values have gained attention as potential alternatives to p-values as measures of uncertainty, significance and evidence. E-values have many advantages over p-values such as validity under optional stopping and arbitrary dependence. We use e-values to construct a model-free backtest of the Expected Shortfall, the most important risk measure in finance and insurance.
报告地点：腾讯会议（会议ID：451 699 821）