Value-at-risk estimation by LS-SVR and FS-LS-SVR based on GAS model |
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| Authors: | Asma Nani Imed Gamoudi Mohamed El Ghourabi |
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| Affiliation: | 1. Quantitative Analysis Research Group (Q.U.A.R.G.), University of Manouba, Manouba, Tunisianani.asma@gmail.com;3. Quantitative Analysis Research Group (Q.U.A.R.G.), University of Manouba, Manouba, Tunisia;4. Department of Management Information Systems, College of Business Administration, Taibah University, Almadinah Almunawarrah, Saudi Arabia;5. Department of Finance and Economics, College of Business, University of Jeddah, Makkah, Saudi Arabia |
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| Abstract: | ABSTRACTConditional risk measuring plays an important role in financial regulation and depends on volatility estimation. A new class of parameter models called Generalized Autoregressive Score (GAS) model has been successfully applied for different error's densities and for different problems of time series prediction in particular for volatility modeling and VaR estimation. To improve the estimating accuracy of the GAS model, this study proposed a semi-parametric method, LS-SVR and FS-LS-SVR applied to the GAS model to estimate the conditional VaR. In particular, we fit the GAS(1,1) model to the return series using three different distributions. Then, LS-SVR and FS-LS-SVR approximate the GAS(1,1) model. An empirical research was performed to illustrate the effectiveness of the proposed method. More precisely, the experimental results from four stock indexes returns suggest that using hybrid models, GAS-LS-SVR and GAS-FS-LS-SVR provides improved performances in the VaR estimation. |
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| Keywords: | Conditional risk artificial intelligence models sparseness asymmetric laplace distribution generalized error distribution |
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