文献类型：期刊文献

英文题名：Early warning of large volatilities based on recurrence interval analysis in Chinese stock markets

作者：Jiang, Zhi-Qiang[1,2,3];Canabarro, Askery[2,3,4];Podobnik, Boris[5,6];Stanley, H. Eugene[2,3];Zhou, Wei-Xing[1]

机构：[1]East China Univ Sci & Technol, Sch Business & Res Ctr Econophys, Shanghai 200237, Peoples R China;[2]Boston Univ, Dept Phys, Boston, MA 02215 USA;[3]Boston Univ, Ctr Polymer Studies, Boston, MA 02215 USA;[4]Univ Fed Alagoas, Nucleo Ciencias Exatas, Grp Fis Mat Condensada, Campus Arapiraca, BR-57309005 Arapiraca, AL, Brazil;[5]Univ Rijeka, Fac Sch Engn, Rijeka 51000, Croatia;[6]Zagreb Sch Econ & Management, Zagreb 10000, Croatia

年份：2016

卷号：16

期号：11

起止页码：1713

外文期刊名：QUANTITATIVE FINANCE

收录：;WOS:【SSCI(收录号:WOS:000385949000008)，SCI-EXPANDED(收录号:WOS:000385949000008)】；

基金：Z.-Q.J. and W.-X.Z. was supported by the National Natural Science Foundation of China [71131007 and 71532009], Shanghai 'Chen Guang' Project [2012CG34], Program for Changjiang Scholars and Innovative Research Team in University [IRT1028], China Scholarship Council [201406745014] and the Fundamental Research Funds for the Central Universities. A.C. acknowledges the support from Brazilian agencies FAPEAL [PPP20110902-011-0025-0069/60030-733/2011] and CNPq [PDE 20736012014-6]. H.E.S. was supported by NSF [Grants CMMI 1125290, PHY 1505000, and CHE-1213217] and by DOE Contract [DE-AC07-05Id14517].

语种：英文

外文关键词：Extreme volatility; Risk estimation; Recurrence interval; Large volatility forecasting; Distribution; Hazard probability

摘要：Forecasting extreme volatility is a central issue in financial risk management. We present a large volatility predicting method based on the distribution of recurrence intervals between successive volatilities exceeding a certain threshold Q, which has a one-to-one correspondencewith the expected recurrence time tau(Q). We find that the recurrence intervals with large tau(Q) are well approximated by the stretched exponential distribution for all stocks. Thus, an analytical formula for determining the hazard probability W(Delta t vertical bar t) that a volatility above Q will occur within a short interval Delta t if the last volatility exceeding Q happened t periods ago can be directly derived from the stretched exponential distribution, which is found to be in good agreement with the empirical hazard probability from real stock data. Using these results, we adopt a decision-making algorithm for triggering the alarm of the occurrence of the next volatility above Q based on the hazard probability. Using the 'receiver operator characteristic' analysis, we find that this prediction method efficiently forecasts the occurrence of large volatility events in real stock data. Our analysis may help us better understand reoccurring large volatilities and quantify more accurately financial risks in stock markets.