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Alternative Modeling for Long Term Risk

Author

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  • Dominique Guegan

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - ENPC - École des Ponts ParisTech - ENS Paris - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique - EHESS - École des hautes études en sciences sociales - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Xin Zhao

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

In this paper, we propose an alternative approach to estimate long-term risk. Instead of using the static square root method, we use a dynamic approach based on volatility forecasting by non-linear models. We explore the possibility of improving the estimations by different models and distributions. By comparing the estimations of two risk measures, value at risk and expected shortfall, with different models and innovations at short, median and long-term horizon, we find out that the best model varies with the forecasting horizon and the generalized Pareto distribution gives the most conservative estimations with all the models at all the horizons. The empirical results show that the square root method underestimates risk at long horizon and our approach is more competitive for risk estimation at long term.

Suggested Citation

  • Dominique Guegan & Xin Zhao, 2012. "Alternative Modeling for Long Term Risk," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00694449, HAL.
  • Handle: RePEc:hal:cesptp:halshs-00694449
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00694449
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    Keywords

    expect shortfall; Value at Risk; Long memory; extreme value distribution; extreme value distribution.; distributions de valeurs extrêmes; Longue mémoire;
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