Double-index VaR model and skewed distribution of indices
Value at Risk (VaR) is widely used in many financial institutions to measure portfolio risk. In our project, we examine if the single-index model under RM methodology that assumes normally distributed returns can be improved on. We try using—1) a skew-normal or skew-t distribution for the index r...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
2009
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/15156 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Value at Risk (VaR) is widely used in many financial institutions to measure portfolio risk. In our project, we examine if the single-index model under RM methodology that assumes normally distributed returns can be improved on. We try using—1) a skew-normal or skew-t distribution for the index returns instead of the normal assumption; 2) GARCH(1,1) to model volatility of the indices; and 3) a double-index model using two indices. We find that skew t distribution outperforms the normal and skew normal distribution in VaR estimates. The skew normal does not necessarily give better VaR estimates than the normal distribution, except for the double-index case. GARCH outperforms RM for in-sample data, but not for out-sample predictions. Finally, the double-index model performs better than the single-index model under skew normal assumptions, but not under normal assumptions. |
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