Extreme-quantile tracking for financial time series

Valérie Chavez-Demoulin, Paul Embrechts and Sylvain Sardy
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Valérie Chavez-Demoulin: University of Lausanne
Paul Embrechts: ETH Zurich and Swiss Finance Institute
Sylvain Sardy: University of Geneva

No 11-27, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: Time series of financial asset values exhibit well known statistical features such as heavy tails and volatility clustering. Strongly present in some series, nonstationarity is a feature that has been somewhat overlooked. This may however be a highly relevant feature when estimating extreme quantiles (VaR) for such series. We propose a nonparametric extension of the classical Peaks-Over-Threshold method to fit the time varying volatility in situations where the stationarity assumption is strongly violated by erratic changes of regime. A back testing study for the UBS share price over the subprime crisis reveals that our approach provides better extreme-quantile (VaR) estimates than methods that ignore nonstationarity.

Keywords: Bayesian analysis; Markov random field; Financial time series; Generalized Pareto distribution; Peaks-Over-Threshold; Regime Switching; Statistics of extremes; Value-at-Risk. (search for similar items in EconPapers)
JEL-codes: C11 C14 C22 G10 G21 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2011-07
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp1127

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