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Alternative modeling for long term risk

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  • Dominique Guégan
  • Xin Zhao

Abstract

In this paper, we propose an alternative approach to estimate long-term risk. Instead of using the static square root of time method, we use a dynamic approach based on volatility forecasting by non-linear models. We explore the possibility of improving the estimations using 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 that the best model varies with the forecasting horizon and that 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 horizons and our approach is more competitive for risk estimation over a long term.

Suggested Citation

  • Dominique Guégan & Xin Zhao, 2014. "Alternative modeling for long term risk," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2237-2253, December.
  • Handle: RePEc:taf:quantf:v:14:y:2014:i:12:p:2237-2253
    DOI: 10.1080/14697688.2013.835860
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    File URL: http://hdl.handle.net/10.1080/14697688.2013.835860
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    References listed on IDEAS

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    1. Dominique Guegan & Bertrand K. Hassani, 2011. "Operational risk: A Basel II++ step before Basel III," Documents de travail du Centre d'Economie de la Sorbonne 11053r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Mar 2012.
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    Cited by:

    1. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.

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