文献类型：期刊文献

英文题名：Predicting tail events in a RIA-EVT-Copula framework

作者：Li, Wei-Zhen[1]; Zhai, Jin-Rui[1]; Jiang, Zhi-Qiang[1,2]; Wang, Gang-Jin[3]; Zhou, Wei-Xing[1,2]

机构：[1] School of Business, East China University of Science and Technology, Shanghai, 200237, China; [2] Research Center for Econophysics, East China University of Science and Technology, Shanghai, 200237, China; [3] Business School, Center of Finance and Investment Management, Hunan University, Changsha, 410082, China

年份：2020

外文期刊名：arXiv

收录：EI(收录号：20200507442)

语种：英文

外文关键词：Decision making - Finance - Hazards - Pareto principle - Risk management

摘要：Predicting the occurrence of tail events is of great importance in financial risk management. By employing the method of peak-over-threshold (POT) to identify the financial extremes, we perform a recurrence interval analysis (RIA) on these extremes. We find that the waiting time between consecutive extremes (recurrence interval) follow a q-exponential distribution and the sizes of extremes above the thresholds (exceeding size) conform to a generalized Pareto distribution. We also find that there is a significant correlation between recurrence intervals and exceeding sizes. We thus model the joint distribution of recurrence intervals and exceeding sizes through connecting the two corresponding marginal distributions with the Frank and AMH copula functions, and apply this joint distribution to estimate the hazard probability to observe another extreme in ?t time since the last extreme happened t time ago. Furthermore, an extreme predicting model based on RIA-EVT-Copula is proposed by applying a decision-making algorithm on the hazard probability. Both in-sample and out-of-sample tests reveal that this new extreme forecasting framework has better performance in prediction comparing with the forecasting model based on the hazard probability only estimated from the distribution of recurrence intervals. Our results not only shed a new light on understanding the occurring pattern of extremes in financial markets, but also improve the accuracy to predict financial extremes for risk management. Copyright ? 2020, The Authors. All rights reserved.