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    Dependent bootstrapping for value-at-risk and expected shortfall

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    Authors
    Laker, I.
    Huang, Chun-Kai
    Clark, A.
    Date
    2017
    Type
    Journal Article
    
    Metadata
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    Citation
    Laker, I. and Huang, C. and Clark, A. 2017. Dependent bootstrapping for value-at-risk and expected shortfall. Risk Management. 19 (4): pp. 301-322.
    Source Title
    Risk Management
    DOI
    10.1057/s41283-017-0023-y
    ISSN
    1460-3799
    URI
    http://hdl.handle.net/20.500.11937/66949
    Collection
    • Curtin Research Publications
    Abstract

    Estimation in extreme financial risk is often faced with challenges such as the need for adequate distributional assumptions, considerations for data dependencies, and the lack of tail information. Bootstrapping provides an alternative that overcomes some of these challenges. It does not assume a distributional form and asymptotically replicates the empirical density for resampled data. Moreover, advanced bootstrapping can cater for dependencies and stationarity in the data. In this paper, we evaluate the use of dependent bootstrapping, both for the original financial time series and for its GARCH innovations (under the Gaussian and Student t noise assumptions), in forecasting value-at-risk and expected shortfall. We also assess the effect of using different window sizes for these procedures. The two datasets used are daily returns of the S & P500 from NYSE and the ALSI from JSE.

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