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Dependent bootstrapping for value-at-risk and expected shortfall

Author

Listed:
  • Ian Laker

    (University of Cape Town)

  • Chun-Kai Huang

    (University of Cape Town)

  • Allan Ernest Clark

    (University of Cape Town)

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.

Suggested Citation

  • Ian Laker & Chun-Kai Huang & Allan Ernest Clark, 2017. "Dependent bootstrapping for value-at-risk and expected shortfall," Risk Management, Palgrave Macmillan, vol. 19(4), pages 301-322, November.
    • Handle: RePEc:pal:risman:v:19:y:2017:i:4:d:10.1057_s41283-017-0023-y
    DOI: 10.1057/s41283-017-0023-y
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    References listed on IDEAS

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    More about this item

    Keywords

    Block bootstrap; Stationary bootstrap; Value-at-risk; Expected shortfall; GARCH;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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