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The application of extreme value theory in value at risk estimation:The case of liability insurance claims in Iran insurance company
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Ghadi Mahdavi   , Zahra Majedi * |
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Abstract: (20668 Views) |
The GARCH(1,1) and GARCH(1,1)-t models lead to highly volatile quantile forecasts, while historical simulation, Variance–Covariance, adaptive generalized Pareto distribution and non-adaptive generalized Pareto distribution models provide more stable quantile forecasts. In general, GARCH(1,1)-t, generalized Pareto distribution models and historical simulation are preferable for most quantiles. |
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| Keywords: Value-at-risk, Extreme value theory, GARCH model, Historical simulation, Variance- Covariance |
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Full-Text [PDF 2123 kb]
(3996 Downloads)
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Type of Study: Applied |
Subject:
Applied Statistics
Received: 2011/11/28 | Accepted: 2011/11/29 | Published: 2020/02/18
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