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A Bootstrap Method with Importance Resampling to Evaluate Value-at-Risk
Journal of Financial Studies
View Archive Info| Field | Value | |
| Title |
A Bootstrap Method with Importance Resampling to Evaluate Value-at-Risk
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| Creator |
Shih-Kuei Lin
Cheng-Der Fuh Tze-Jieh Ko |
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| Description |
To evaluate a portfolio value-at-risk (VaR), Monte Carlo analysis is by far the most powerful method. However, the biggest drawback of this method is its computational time. In this paper, we model the return of risk factors with a multivariate normal as well as a multivariate t distribution, and provide an efficient method, a bootstrap algorithm with importance resampling, to estimate portfolio loss probability and portfolio value-at-risk. In the simulation study and sensitivity analysis of the bootstrap method, we first note that the estimate for the quantile and tail probability with importance resampling is more efficient than the naive Monte Carlo method. Next, we observe that the estimates of the quantile and the tail probability are not sensitive to the estimated parameters for the multivariate normal and the multivariate t distribution. As an illustration of our proposed methods, we report an empirical study based on two stock index returns in Taiwan, the Chang Hwa Bank and the China Steel Corporation. Key words:Value-at-risk, Heavy-tailed, Bootstrap, Importance resampling, Variance reduction, Multivariate normal distribution, Multivariate t distribution, Monte Carlo simulation. |
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| Publisher |
Journal of Financial Studies
財務金èžå¸åˆŠ |
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| Date |
2011-06-10
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| Type |
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| Format |
application/pdf
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| Identifier |
http://www.jfs.org.tw/index.php/jfs/article/view/2011159
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| Source |
Journal of Financial Studies; Vol 12, No 1 (2004); 81
財務金èžå¸åˆŠ; Vol 12, No 1 (2004); 81 |
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| Language |
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