Early warning of large volatilities based on recurrence interval analysis in Chinese stock markets. Issue 11 (1st November 2016)

• Record Type: Journal Article
• Authors: Jiang, Zhi-Qiang; Canabarro, Askery; Podobnik, Boris; Stanley, H. Eugene; Zhou, Wei-Xing
• Is Part Of: Quantitative finance. Volume 16:Issue 11(2016)
• Page Start: 1713
• Page End: 1724
• Publication Date: 2016-11-01
• Subjects: Extreme volatility -- Risk estimation -- Recurrence interval -- Large volatility forecasting -- Distribution -- Hazard probability; G130 -- G110; Finance -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Investments -- Mathematics -- Periodicals; Economics -- Periodicals; Finances -- Modèles mathématiques -- Périodiques; 332.015118
• DOI: 10.1080/14697688.2016.1175656
• Languages: English
• ISSNs: 1469-7688

Abstract

Abstract : 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 correspondence with the expected recurrence time . We find that the recurrence intervals with large are well approximated by the stretched exponential distribution for all stocks. Thus, an analytical formula for determining the hazard probability that a volatility above Q will occur within a short interval 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.