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Contribution Details
| Type | Journal Article |
| Scope | Contributions to practice |
| Title | Accurate value-at-risk forecasting based on the Normal-GARCH model |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Computational Statistics & Data Analysis |
| Publisher | Elsevier |
| Geographical Reach | international |
| ISSN | 0167-9473 |
| Volume | 51 |
| Number | 4 |
| Page Range | 2295 - 2312 |
| Date | 2006 |
| Abstract Text | A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal-GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and, except for having to choose a window length L for the bias-correction step, fully data driven. The results for several different financial asset returns over a long out-of-sample forecasting period, as well as use of simulated data, strongly support use of the new method, and the performance is not sensitive to the choice of L. |
| Official URL | http://www.sciencedirect.com/science/article/pii/S0167947306003367 |
| Digital Object Identifier | 10.1016/j.csda.2006.09.017 |
| Other Identification Number | merlin-id:4466 |
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