Alternative Modeling for Long Term Risk

Dominique Guegan () and Xin Zhao ()
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Dominique Guegan: Centre d'Economie de la Sorbonne - Paris School of Economics, https://cv.archives-ouvertes.fr/dominique-guegan
Xin Zhao: Centre d'Economie de la Sorbonne, https://centredeconomiesorbonne.cnrs.fr

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

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

Keywords: Long memory; Value at Risk; expect shortfall; extreme value distribution (search for similar items in EconPapers)
JEL-codes: C58 G17 G32 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2012-03
New Economics Papers: this item is included in nep-ban, nep-for, nep-rmg and nep-upt
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http://mse.univ-paris1.fr/pub/mse/CES2012/12025.pdf (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:12025

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