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Combining Value-at-Risk forecasts using penalized quantile regressions

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  • Bayer, Sebastian

Abstract

Penalized quantile regressions are proposed for the combination of Value-at-Risk forecasts. The primary reason for regularization of the quantile regression estimator with the elastic net, lasso and ridge penalties is multicollinearity among the standalone forecasts, which results in poor forecast performance of the non-regularized estimator due to unstable combination weights. This new approach is applied to combining the Value-at-Risk forecasts of a wide range of frequently used risk models for stocks comprising the Dow Jones Industrial Average Index. Within a thorough comparison analysis, the penalized quantile regressions perform better in terms of backtesting and tick losses than the standalone models and several competing forecast combination approaches. This is particularly evident during the global financial crisis of 2007–2008.

Suggested Citation

  • Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
  • Handle: RePEc:eee:ecosta:v:8:y:2018:i:c:p:56-77
    DOI: 10.1016/j.ecosta.2017.08.001
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    5. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "High Dimensional Forecast Combinations Under Latent Structures," Papers 2010.09477, arXiv.org.
    6. Szymon Lis & Marcin Chlebus, 2021. "Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts," Working Papers 2021-11, Faculty of Economic Sciences, University of Warsaw.
    7. Jung-Bin Su & Jui-Cheng Hung, 2018. "The Value-At-Risk Estimate of Stock and Currency-Stock Portfolios’ Returns," Risks, MDPI, Open Access Journal, vol. 6(4), pages 1-42, November.
    8. De Gooijer Jan G. & Zerom Dawit, 2020. "Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts," Journal of Time Series Econometrics, De Gruyter, vol. 12(1), pages 1-15, January.

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    More about this item

    Keywords

    Value-at-Risk; Forecast combination; Quantile regression; Elastic net; Regularization;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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