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Pair Copula Construction based Expected Shortfall estimation

Author

Listed:
  • Marcelo Brutti Righi

    (Federal University of Santa Maria)

  • Paulo Sergio Ceretta

    (Federal University of Santa Maria)

Abstract

In this note we present an algorithm for portfolio ES estimation through Pair Copula Construction. The advantages of this method are the flexibility in what dependence structure is determined, as well as the simplicity of simulation procedures. We illustrate our approach with a brief empirical application with international market indices during a crisis period, comparing with other techniques which are largely applied.

Suggested Citation

  • Marcelo Brutti Righi & Paulo Sergio Ceretta, 2013. "Pair Copula Construction based Expected Shortfall estimation," Economics Bulletin, AccessEcon, vol. 33(2), pages 1067-1072.
    • Handle: RePEc:ebl:ecbull:eb-13-00142
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    File URL: http://www.accessecon.com/Pubs/EB/2013/Volume33/EB-13-V33-I2-P100.pdf
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    References listed on IDEAS

    as
    1. James W. Taylor, 2008. "Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall," Journal of Financial Econometrics, Oxford University Press, vol. 6(3), pages 382-406, Summer.
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    4. Lorán Chollete & Andréas Heinen & Alfonso Valdesogo, 2009. "Modeling International Financial Returns with a Multivariate Regime-switching Copula," Journal of Financial Econometrics, Oxford University Press, vol. 7(4), pages 437-480, Fall.
    5. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    6. Song Xi Chen, 2008. "Nonparametric Estimation of Expected Shortfall," Journal of Financial Econometrics, Oxford University Press, vol. 6(1), pages 87-107, Winter.
    7. Leorato, Samantha & Peracchi, Franco & Tanase, Andrei V., 2012. "Asymptotically efficient estimation of the conditional expected shortfall," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 768-784.
    8. Marcelo Brutti Righi & Paulo Sergio Ceretta, 2012. "Predicting the risk of global portfolios considering the non-linear dependence structures," Economics Bulletin, AccessEcon, vol. 32(1), pages 282-294.
    9. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    10. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    11. Zhu, Dongming & Galbraith, John W., 2011. "Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 765-778, September.
    12. Marcelo Brutti Righi & Paulo Sergio Ceretta, 2011. "Analyzing the structural behavior of volatility in the Major European Markets during the Greek crisis," Economics Bulletin, AccessEcon, vol. 31(4), pages 3016-3029.
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    Cited by:

    1. Fernanda Maria Müller & Marcelo Brutti Righi, 2024. "Comparison of Value at Risk (VaR) Multivariate Forecast Models," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 75-110, January.
    2. Fernanda Maria Müller & Marcelo Brutti Righi, 2018. "Numerical comparison of multivariate models to forecasting risk measures," Risk Management, Palgrave Macmillan, vol. 20(1), pages 29-50, February.
    3. Fernanda Maria Müller & Thalles Weber Gössling & Samuel Solgon Santos & Marcelo Brutti Righi, 2024. "A comparison of Range Value at Risk (RVaR) forecasting models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 509-543, April.

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    More about this item

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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