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Forecasting Value-at-Risk under Different Distributional Assumptions

Author

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  • 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.)

Abstract

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.

Suggested Citation

  • Manuela Braione & Nicolas K. Scholtes, 2016. "Forecasting Value-at-Risk under Different Distributional Assumptions," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-27, January.
  • Handle: RePEc:gam:jecnmx:v:4:y:2016:i:1:p:3-:d:61992
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    References listed on IDEAS

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    1. repec:gam:jrisks:v:5:y:2017:i:3:p:45-:d:110079 is not listed on IDEAS
    2. repec:taf:apeclt:v:24:y:2017:i:21:p:1613-1620 is not listed on IDEAS
    3. Degiannakis, Stavros & Potamia, Artemis, 2017. "Multiple-days-ahead value-at-risk and expected shortfall forecasting for stock indices, commodities and exchange rates: Inter-day versus intra-day data," International Review of Financial Analysis, Elsevier, vol. 49(C), pages 176-190.
    4. repec:rjr:romjef:v::y:2017:i:4:p:97-115 is not listed on IDEAS

    More about this item

    Keywords

    Value-at-Risk; forecast accuracy; distributions; backtesting;

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C - Mathematical and Quantitative Methods
    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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