Extreme-quantile tracking for financial time series

V. Chavez-Demoulin, P. Embrechts and S. Sardy

Journal of Econometrics, 2014, vol. 181, issue 1, 44-52

Abstract: Time series of financial asset values exhibit well-known statistical features such as heavy tails and volatility clustering. We propose a nonparametric extension of the classical Peaks-Over-Threshold method from extreme value theory to fit the time varying volatility in situations where the stationarity assumption may be violated by erratic changes of regime, say. As a result, we provide a method for estimating conditional risk measures applicable to both stationary and nonstationary series. A backtesting study for the UBS share price over the subprime crisis exemplifies our approach.

Keywords: Bayesian analysis; Conditional risk measures; Financial time series; Generalized Pareto distribution; Markov random field; Peaks-Over-Threshold; Quantile estimation; Regime switching; Statistics of extremes; Value-at-risk (search for similar items in EconPapers)
JEL-codes: C G (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (33)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:181:y:2014:i:1:p:44-52

DOI: 10.1016/j.jeconom.2014.02.007

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