Short term prediction of extreme returns based on the recurrence interval analysis
H. Eugene Stanley and
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Zhi-Qiang Jiang: ECUST, BU
Askery Canabarro: BU, UFA
Boris Podobnik: ZSEM
Chi Xie: HNU
H. Eugene Stanley: BU
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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 on 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 $W(\Delta t |t)$. Maximizing the usefulness of extreme forecasts to define an optimized hazard threshold, we indicates 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.
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Published in Quantitative Finance 18 (3), 353-370 (2018)
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Journal Article: Short term prediction of extreme returns based on the recurrence interval analysis (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1610.08230
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