Short term prediction of extreme returns based on the recurrence interval analysis. Issue 3 (4th March 2018)

• Record Type: Journal Article
• Authors: Jiang, Zhi-Qiang; Wang, Gang-Jin; Canabarro, Askery; Podobnik, Boris; Xie, Chi; Stanley, H. Eugene; Zhou, Wei-Xing
• Is Part Of: Quantitative finance. Volume 18:Issue 3(2018)
• Page Start: 353
• Page End: 370
• Publication Date: 2018-03-04
• Subjects: Extreme return -- Risk estimation -- Recurrence interval -- Return forecasting -- 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: 10.1080/14697688.2017.1373843
• Languages: English
• ISSNs: 1469-7688

Abstract

Abstract : Being able to predict the occurrence of extreme returns is important in financial risk management. Using the distribution of recurrence intervals—the waiting time between consecutive extremes—we show that these extreme returns are predictable in the short term. Examining a range of different types of returns and thresholds we find that recurrence intervals follow a q -exponential distribution, which we then use to theoretically derive the hazard probability . Maximizing the usefulness of extreme forecasts to define an optimized hazard threshold, we indicate a financial extreme occurring within the next day when the hazard probability is greater than the optimized threshold. Both in-sample tests and out-of-sample predictions indicate that these forecasts are more accurate than a benchmark that ignores the predictive signals. This recurrence interval finding deepens our understanding of reoccurring extreme returns and can be applied to forecast extremes in risk management.