文澜金融论坛（第256期）

Abstract:

Empirical evidence has shown that in terms of market uncertainty, both maxima and minima of cross-sectional stock/asset returns are driving forces. Due to the asymptotic independent property of maxima and minima of a sequence of independent random variables, existing stochastic models often only focus on one of them. This paper proposes a new dynamic stochastic model, dual-maxima-minima autoregressive conditional Frechet (DMMAcF) model, to jointly fit cross-sectional maxima and minima, and then to quantify market uncertainty through a time-varying tail risk measure and a pair of dual implied volatilities. The scale parameters (analog to volatilities) and the shape parameters (analog to volatilities of volatilities) in Frechet distributions vary conditionally on the past information. The DMMAcF model possesses unique properties such as better and faster risk estimation. Numerical experiments confirm these desired market properties. It is found that the early market crash warning effect of the DMMAcF model reacts better than existing models. (Joint work with Yu Chen and Tiantian Mao)

主讲人介绍：

张正军，美国威斯康星大学计算机科学、信息、数据科学学院教授，国际数理统计学院会士和执行委员会委员，美国统计学会会士，担任国际顶级期刊Journal of Business & Economic Statistics副主编、Journal of Econometrics金融工程与风险管理特刊共同主编等多个国际经济统计期刊的特刊主编和副主编。主要研究方向包括：极值理论、金融时间序列、金融风险、基于汇率的数字货币、非线性因果推断、稀有事件概率建模、极端气候问题、医学统计中的关键癌症基因识别。