On conditional risk estimation considering model risk |
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Authors: | Fedya Telmoudi Mohamed EL Ghourabi Mohamed Limam |
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Affiliation: | 1. LARODEC, University of Tunis, 41, Avenue de la Liberté, Cité Bouchoucha, Le Bardo 2000 Tunis, Tunisia;2. Department of Economics and Quantitative Methods, ESSECT, University of Tunis, 4, Rue Abou Zakaria Hafsi, 1089 Montfleury, Tunis, Tunisia;3. Department of Management Information Systems, Dhofar University, Salalah, Oman |
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Abstract: | Usually, parametric procedures used for conditional variance modelling are associated with model risk. Model risk may affect the volatility and conditional value at risk estimation process either due to estimation or misspecification risks. Hence, non-parametric artificial intelligence models can be considered as alternative models given that they do not rely on an explicit form of the volatility. In this paper, we consider the least-squares support vector regression (LS-SVR), weighted LS-SVR and Fixed size LS-SVR models in order to handle the problem of conditional risk estimation taking into account issues of model risk. A simulation study and a real application show the performance of proposed volatility and VaR models. |
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Keywords: | artificial intelligence models conditional value at risk LS-SVR GARCH models modelrisk spareness |
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