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Extreme-quantile tracking for financial time series

Author

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

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.

Suggested Citation

  • Chavez-Demoulin, V. & Embrechts, P. & Sardy, S., 2014. "Extreme-quantile tracking for financial time series," Journal of Econometrics, Elsevier, vol. 181(1), pages 44-52.
    • Handle: RePEc:eee:econom:v:181:y:2014:i:1:p:44-52
    DOI: 10.1016/j.jeconom.2014.02.007
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    References listed on IDEAS

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    More about this item

    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;
    All these keywords.

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • C - Mathematical and Quantitative Methods
    • C - Mathematical and Quantitative Methods
    • G - Financial Economics
    • G - Financial Economics

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