统计学系系列讲座之280期

时间：2018年 1月4日（星期四）16:00-17:00

地点：史带楼205室

主持人：张新生 教授 复旦大学管理学院统计学系

主题：While we are talking about systematic risk, do we have a probabilistic definition and a statistical solution for it?

主讲人：Professor Zhengjun Zhang（张正军）

Department of Statistics, University of Wisconsin，Madison

简介：张正军教授目前担任复旦大学大数据学院院长助理，北京大学经济学院兼职教授。他是Journal of Business & Economic Statistics和 Statistics and Its Interface等国际著名统计期刊的Associate editor。其主要研究方向为：Nonlinear Financial Time Series Analysis，Extreme Value Analytics for Big Data，Risk Analysis in Finance。

摘要：This talk introduces a novel dynamic generalized extreme value~(GEV) framework for modeling the time-varying behavior of maxima in financial time series. Specifically, an autoregressive conditional Fr\'echet~(AcF) model is proposed in which the maxima are modeled by a Fr\'echet distribution with time-varying scale parameter~(volatility) and shape parameter~(tail index) conditioned on past information. The AcF provides a direct and accurate modeling of the time-varying behavior of maxima and offers a new angle to study the tail risk dynamics in financial markets. Probabilistic properties of AcF are studied, and a maximum likelihood estimator is used for model estimation, with its statistical properties investigated. Simulations show the flexibility of AcF and confirm the reliability of its estimators. Two real data examples on cross-sectional stock returns and high-frequency foreign exchange returns are used to demonstrate the AcF modeling approach, where significant improvement over the static GEV has been observed for market tail risk monitoring and conditional VaR estimation. Empirical result of AcF is consistent with the findings of the dynamic peak-over-threshold~(POT) literature that the tail index of financial markets varies through time. (Joint work with Zifeng Zhao and Rong Chen).

    统计学系 

2018-1-3