Version: Final published version
Article: article from journal or magazin.
Extreme-quantile tracking for financial time series
Journal of Econometrics
Available online 26 February 2014
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.
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
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