Dependent bootstrapping for value-at-risk and expected shortfall
Estimation in extreme financial risk is often faced with challenges such as the need for adequate distributional assumptions, considerations for data dependencies, and the lack of tail information. Bootstrapping provides an alternative that overcomes some of these challenges. It does not assume a di...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/66949 |
| _version_ | 1848761435049426944 |
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| author | Laker, I. Huang, Chun-Kai Clark, A. |
| author_facet | Laker, I. Huang, Chun-Kai Clark, A. |
| author_sort | Laker, I. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Estimation in extreme financial risk is often faced with challenges such as the need for adequate distributional assumptions, considerations for data dependencies, and the lack of tail information. Bootstrapping provides an alternative that overcomes some of these challenges. It does not assume a distributional form and asymptotically replicates the empirical density for resampled data. Moreover, advanced bootstrapping can cater for dependencies and stationarity in the data. In this paper, we evaluate the use of dependent bootstrapping, both for the original financial time series and for its GARCH innovations (under the Gaussian and Student t noise assumptions), in forecasting value-at-risk and expected shortfall. We also assess the effect of using different window sizes for these procedures. The two datasets used are daily returns of the S & P500 from NYSE and the ALSI from JSE. |
| first_indexed | 2025-11-14T10:31:37Z |
| format | Journal Article |
| id | curtin-20.500.11937-66949 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:31:37Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-669492018-09-21T00:31:52Z Dependent bootstrapping for value-at-risk and expected shortfall Laker, I. Huang, Chun-Kai Clark, A. Estimation in extreme financial risk is often faced with challenges such as the need for adequate distributional assumptions, considerations for data dependencies, and the lack of tail information. Bootstrapping provides an alternative that overcomes some of these challenges. It does not assume a distributional form and asymptotically replicates the empirical density for resampled data. Moreover, advanced bootstrapping can cater for dependencies and stationarity in the data. In this paper, we evaluate the use of dependent bootstrapping, both for the original financial time series and for its GARCH innovations (under the Gaussian and Student t noise assumptions), in forecasting value-at-risk and expected shortfall. We also assess the effect of using different window sizes for these procedures. The two datasets used are daily returns of the S & P500 from NYSE and the ALSI from JSE. 2017 Journal Article http://hdl.handle.net/20.500.11937/66949 10.1057/s41283-017-0023-y restricted |
| spellingShingle | Laker, I. Huang, Chun-Kai Clark, A. Dependent bootstrapping for value-at-risk and expected shortfall |
| title | Dependent bootstrapping for value-at-risk and expected shortfall |
| title_full | Dependent bootstrapping for value-at-risk and expected shortfall |
| title_fullStr | Dependent bootstrapping for value-at-risk and expected shortfall |
| title_full_unstemmed | Dependent bootstrapping for value-at-risk and expected shortfall |
| title_short | Dependent bootstrapping for value-at-risk and expected shortfall |
| title_sort | dependent bootstrapping for value-at-risk and expected shortfall |
| url | http://hdl.handle.net/20.500.11937/66949 |