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Robust Value at Risk Prediction

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  • Loriano Mancini
  • Fabio Trojani

Abstract

This paper proposes a robust semiparametric bootstrap method to estimate predictive distributions of GARCH-type models. The method is based on a robust estimation of parametric GARCH models and a robustified resampling scheme for GARCH residuals that controls bootstrap instability due to outlying observations. A Monte Carlo simulation shows that our robust method provides more accurate Value at Risk (VaR) forecasts than classical methods, often by a large extent, especially for several days ahead horizons and/or in presence of outlying observations. An empirical application confirms the simulation results. The robust procedure outperforms in backtesting several other VaR prediction methods, such as RiskMetrics, CAViaR, historical simulation, and classical filtered historical simulation methods. We show empirically that robust estimation reduces tail estimation risk, providing more accurate and more stable VaR prediction intervals over time. Copyright The Author 2011. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Suggested Citation

  • Loriano Mancini & Fabio Trojani, 2011. "Robust Value at Risk Prediction," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 9(2), pages 281-313, Spring.
  • Handle: RePEc:oup:jfinec:v:9:y:2011:i:2:p:281-313
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbq035
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    Citations

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    Cited by:

    1. Dias, Alexandra, 2013. "Market capitalization and Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5248-5260.
    2. Hotta, Luiz & Trucíos, Carlos & Ruiz, Esther, 2015. "Robust bootstrap forecast densities for GARCH models: returns, volatilities and value-at-risk," DES - Working Papers. Statistics and Econometrics. WS ws1523, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Francq, Christian & Zakoian, Jean-Michel, 2015. "Joint inference on market and estimation risks in dynamic portfolios," MPRA Paper 68100, University Library of Munich, Germany.
    4. Abad, Pilar & Benito, Sonia, 2013. "A detailed comparison of value at risk estimates," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 258-276.
    5. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    6. Kellner, Ralf & Rösch, Daniel, 2016. "Quantifying market risk with Value-at-Risk or Expected Shortfall? – Consequences for capital requirements and model risk," Journal of Economic Dynamics and Control, Elsevier, vol. 68(C), pages 45-63.
    7. esposito, francesco paolo & cummins, mark, 2015. "Multiple hypothesis testing of market risk forecasting models," MPRA Paper 64986, University Library of Munich, Germany.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other

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