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Bootstrap for Value at Risk Prediction

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  • Meriem Rjiba
  • Michail Tsagris
  • Hedi Mhalla

Abstract

We evaluate the predictive performance of a variety of value-at-risk (VaR) models for a portfolio consisting of five assets. Traditional VaR models such as historical simulation with bootstrap and filtered historical simulation methods are considered. We suggest a new method for estimating Value at Risk: the filtered historical simulation GJR-GARCH method based on bootstrapping the standardized GJR-GARCH residuals. The predictive performance is evaluated in terms of three criteria, the test of unconditional coverage, independence and conditional coverage and the quadratic loss function suggested. The results show that classical methods are in efficient under moderate departures from normality and that the new method produces the most accurate forecasts of extreme losses.

Suggested Citation

  • Meriem Rjiba & Michail Tsagris & Hedi Mhalla, 2015. "Bootstrap for Value at Risk Prediction," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(6), pages 362-371.
    • Handle: RePEc:rss:jnljef:v4i6p4
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    References listed on IDEAS

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    Cited by:

    1. Kakade, Kshitij & Jain, Ishan & Mishra, Aswini Kumar, 2022. "Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach," Resources Policy, Elsevier, vol. 78(C).

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    More about this item

    Keywords

    Value at Risk; bootstrap; GARCH;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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