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dc.contributor.authorLaker, I.
dc.contributor.authorHuang, Chun-Kai
dc.contributor.authorClark, A.
dc.date.accessioned2018-05-18T07:56:43Z
dc.date.available2018-05-18T07:56:43Z
dc.date.created2018-05-18T00:23:15Z
dc.date.issued2017
dc.identifier.citationLaker, I. and Huang, C. and Clark, A. 2017. Dependent bootstrapping for value-at-risk and expected shortfall. Risk Management. 19 (4): pp. 301-322.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/66949
dc.identifier.doi10.1057/s41283-017-0023-y
dc.description.abstract

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.

dc.titleDependent bootstrapping for value-at-risk and expected shortfall
dc.typeJournal Article
dcterms.source.volume19
dcterms.source.number4
dcterms.source.startPage301
dcterms.source.endPage322
dcterms.source.issn1460-3799
dcterms.source.titleRisk Management
curtin.accessStatusFulltext not available


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