Alternative Modeling for Long Term Risk
AbstractIn 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.
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Bibliographic InfoPaper provided by Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne in its series Documents de travail du Centre d'Economie de la Sorbonne with number 12025.
Length: 21 pages
Date of creation: Mar 2012
Date of revision:
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Long memory; Value at Risk; expect shortfall; extreme value distribution.;
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- G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-05-02 (All new papers)
- NEP-BAN-2012-05-02 (Banking)
- NEP-FOR-2012-05-02 (Forecasting)
- NEP-RMG-2012-05-02 (Risk Management)
- NEP-UPT-2012-05-02 (Utility Models & Prospect Theory)
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