Scaling and Memory in Return Loss Intervals: Application to Risk Estimation

Kazuko Yamasaki (), Lev Muchnik, Shlomo Havlin, Armin Bunde and H. Eugene Stanley
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Kazuko Yamasaki: Center for Polymer Studies and Department of Physics Boston University
Lev Muchnik: Bar-Ilan University
Shlomo Havlin: Bar-Ilan University
Armin Bunde: Justus-Liebig-Universität
H. Eugene Stanley: Center for Polymer Studies and Department of Physics Boston University

A chapter in Practical Fruits of Econophysics, 2006, pp 43-51 from Springer

Abstract: Summary We study the statistics of the return intervals τ q between two consecutive return losses below a threshold −q, in various stocks, currencies and commodities. We find the probability distribution function (pdf) of τ q scales with the mean return interval τ q in a quite universal way, which may enable us to extrapolate rare events from the behavior of more frequent events with better statistics. The functional form of the pdf shows deviation from a simple exponential behavior, suggesting memory effects in losses. The memory shows up strongly in the conditional mean loss return intervals which depend significantly on the previous interval. This dependence can be used to improve the estimate of the risk level.

Keywords: return loss intervals; scaling; universality; value-at-risk (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-4-431-28915-9_7

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DOI: 10.1007/4-431-28915-1_7

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