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Extreme-quantile tracking for financial time series

Listed author(s):

    (University of Lausanne)

  • Paul Embrechts

    (ETH Zurich and Swiss Finance Institute)

  • Sylvain Sardy

    (University of Geneva)

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    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.

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    Paper provided by Swiss Finance Institute in its series Swiss Finance Institute Research Paper Series with number 11-27.

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    Length: 34 pages
    Date of creation:
    Handle: RePEc:chf:rpseri:rp1127
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