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Comparing value-at-risk methodologies

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  • Lima, Luiz Renato Regis de Oliveira
  • Neri, Breno de Andrade Pinheiro

Abstract

In this paper, we compare four different Value-at-Risk (V aR) methodologies through Monte Carlo experiments. Our results indicate that the method based on quantile regression with ARCH effect dominates other methods that require distributional assumption. In particular, we show that the non-robust methodologies have higher probability to predict V aRs with too many violations. We illustrate our findings with an empirical exercise in which we estimate V aR for returns of S˜ao Paulo stock exchange index, IBOVESPA, during periods of market turmoil. Our results indicate that the robust method based on quantile regression presents the least number of violations.

Suggested Citation

  • Lima, Luiz Renato Regis de Oliveira & Neri, Breno de Andrade Pinheiro, 2006. "Comparing value-at-risk methodologies," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 629, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    • Handle: RePEc:fgv:epgewp:629
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    Cited by:

    1. Zhijie Xiao & Luiz Renato Lima, 2007. "Testing Covariance Stationarity," Econometric Reviews, Taylor & Francis Journals, vol. 26(6), pages 643-667.
    2. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    3. Allen, David E. & Amram, Ron & McAleer, Michael, 2013. "Volatility spillovers from the Chinese stock market to economic neighbours," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 238-257.
    4. David E. Allen & Michael McAleer & Robert J. Powell & Abhay K. Singh, 2012. "Volatility Spillovers from the US to Australia and China across the GFC," Documentos de Trabajo del ICAE 2012-30, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    5. Allen, David E. & McAleer, Michael & Powell, Robert J. & Singh, Abhay K., 2017. "Volatility Spillovers from Australia's major trading partners across the GFC," International Review of Economics & Finance, Elsevier, vol. 47(C), pages 159-175.
    6. Lima, Luiz Renato & Gaglianone, Wagner Piazza & Sampaio, Raquel M.B., 2008. "Debt ceiling and fiscal sustainability in Brazil: A quantile autoregression approach," Journal of Development Economics, Elsevier, vol. 86(2), pages 313-335, June.
    7. Aymen BEN REJEB & Ousama BEN SALHA & Jaleleddine BEN REJEB, 2012. "Value-at-Risk Analysis for the Tunisian Currency Market: A Comparative Study," International Journal of Economics and Financial Issues, Econjournals, vol. 2(2), pages 110-125.
    8. A. Amendola & V. Candila, 2016. "Evaluation of volatility predictions in a VaR framework," Quantitative Finance, Taylor & Francis Journals, vol. 16(5), pages 695-709, May.

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    JEL classification:

    • 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

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