Short term prediction of extreme returns based on the recurrence interval analysis

Zhi-Qiang Jiang, Gang-Jin Wang, Askery Canabarro, Boris Podobnik, Chi Xie, H. Eugene Stanley and Wei-Xing Zhou
Additional contact information
Zhi-Qiang Jiang: ECUST, BU
Askery Canabarro: BU, UFA
Boris Podobnik: ZSEM
Chi Xie: HNU
H. Eugene Stanley: BU

Papers from arXiv.org

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 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.

Date: 2016-10
New Economics Papers: this item is included in nep-ecm, nep-for and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in Quantitative Finance 18 (3), 353-370 (2018)

Downloads: (external link)
http://arxiv.org/pdf/1610.08230 Latest version (application/pdf)

Related works:
Journal Article: Short term prediction of extreme returns based on the recurrence interval analysis (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1610.08230

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().