时 间：2023年6月26日14:00-16:00
地 点：理科大楼A1514
报告人：埃塞克斯大学助理教授
主持人：李丹萍华东师范大学副教授
摘 要：
In this talk, I will present one of my ongoing papers on Lambda-Value-at-Risk (Lambda-VaR), which is an extension of Value-at-Risk (VaR) by changing the fixed probability level to a function 1-Lambda. Here we will focus on the risk sharing problem related to Lambda-VaR. We first consider the inf-convolution of several Lambda-VaR, and obtain the expression for the inf-convolution and the optimal allocation respectively. Moreover, we investigate the inf-convolution of Lambda-VaR and another risk measure. This risk measure can be either lack of cash-additivity or possessing a different belief from Lambda-VaR, including many commonly used risk functionals such as expected utility, rank-dependent expected utility, probability distortion, CoVaR and CoES. Both the optimal value and optimal allocation have been obtained. Finally, we study a more conservative risk measure Lambda-VaR^+: the inf-convolution of Lambda-VaR^+ and a risk measure that is consistent with the second-order stochastic dominance. This is qualitatively different from the previous one. We obtain an expression of the inf-convolution and find an optimal allocation, where the optimal allocation has a very different expression from the previous one and the one existed in the literature.
报告人简介：
Dr Peng Liu is currently a Lecturer (Assistant Professor) in the University of Essex. Dr Liu received his PhD degree from Nankai University in 2015 and then he took the postdoctoral positions in the University of Lausanne and University of Waterloo respectively before joining the University of Essex. His research interest includes extreme value theory and risk measures. He has published more than 20 papers in probability, mathematical finance, and actuarial science journals such as Mathematics of Operations Research, Mathematical Finance, SIAM Journal on Financial Mathematics, Stochastic Processes and Their Applications, Advances in Applied Probability, and Insurance: Mathematics and Economics.