文献类型：期刊文献

英文题名：Combining a self-exciting point process with the truncated generalized Pareto distribution: An extreme risk analysis under price limits

作者：Ji, Jingru[1];Wang, Donghua[1,2];Xu, Dinghai[3];Xu, Chi[1]

机构：[1]East China Univ Sci & Technol, Sch Business, 130 Meilong Rd,POB 114, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Finance, Shanghai 200237, Peoples R China;[3]Univ Waterloo, Dept Econ, Waterloo, ON N2L 3G1, Canada

年份：2020

卷号：57

起止页码：52

外文期刊名：JOURNAL OF EMPIRICAL FINANCE

收录：;WOS:【SSCI(收录号:WOS:000536300700004)】；

基金：All authors warmly thank the editor, Professor Valkanov, the associate editor and two anonymous referees for their valuable comments and suggestions on the earlier versions of the paper. This research is supported by the National Science Foundation of China [grant numbers: 71171083 and 71771087]. Jingru Ji would also be grateful for the Shenwan Hongyuan Securities and Shanghai Post-doctoral Excellence Program [grant number: 2019086]. Dinghai Xu would like to acknowledge the financial support from the International Research Partnership Grant (IRPG) at University of Waterloo. All errors remain ours.

语种：英文

外文关键词：Self-exciting point process; Truncated generalized Pareto distribution; Predictable marks; Price limits; Branching process

摘要：In this paper, we introduce a general framework of the self-exciting point process with the truncated generalized Pareto distribution to measure the extreme risks in the stock markets under price limits. We incorporate the predictable marks, defined as the variance of mark distribution depending on the previous events via the intensity, into the model setting. The proposed process can well accommodate many important empirical characteristics, such as the thick-tailness, extreme risk clustering and price limits. We derive a closed-form solution for the objective likelihood, based on which the proposed model can be estimated via the standard maximum likelihood estimation algorithm. Furthermore, the closed-form measures of the Value-at-Risk and Expected Shortfall are also derived. For empirical illustration, we use the China Securities Index 300 (with +/- 10% price restriction) in the analysis. In general, the results from both in-sample fitting and out-of-sample forecasting measures show that the proposed process can explain the empirical data well. We also investigate the cascade effect of the China stock market by introducing the branching process to distinguish the endogenous risks from the exogenous risks.