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Forecasting Value-at-Risk under Different Distributional Assumptions

Listed author(s):
  • Manuela Braione

    ()

    (Center for Operations Research and Econometrics (CORE), Université catholique de Louvain, 34 Voie du Romans Pays, B-1348 Louvain-la-Neuve, Belgium
    These authors contributed equally to this work.)

  • Nicolas K. Scholtes

    ()

    (Center for Operations Research and Econometrics (CORE), Université catholique de Louvain, 34 Voie du Romans Pays, B-1348 Louvain-la-Neuve, Belgium
    Center for Research in Finance and Management (CeReFiM), Université de Namur, 62 Rue de Bruxelles, B-5000 Namur, Belgium
    These authors contributed equally to this work.)

Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. These features must be taken into account to produce accurate forecasts of Value-at-Risk (VaR). We provide a comprehensive look at the problem by considering the impact that different distributional assumptions have on the accuracy of both univariate and multivariate GARCH models in out-of-sample VaR prediction. The set of analyzed distributions comprises the normal, Student, Multivariate Exponential Power and their corresponding skewed counterparts. The accuracy of the VaR forecasts is assessed by implementing standard statistical backtesting procedures used to rank the different specifications. The results show the importance of allowing for heavy-tails and skewness in the distributional assumption with the skew-Student outperforming the others across all tests and confidence levels.

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Article provided by MDPI, Open Access Journal in its journal Econometrics.

Volume (Year): 4 (2016)
Issue (Month): 1 (January)
Pages: 1-27

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Handle: RePEc:gam:jecnmx:v:4:y:2016:i:1:p:3-:d:61992
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