Evaluating value-at-risk forecasts

dc.contributor.author	Wied, Dominik
dc.contributor.author	Weiß, Gregor N. F.
dc.contributor.author	Ziggel, Daniel
dc.date.accessioned	2015-04-13T08:48:51Z
dc.date.available	2015-04-13T08:48:51Z
dc.date.issued	2015
dc.description.abstract	We propose two new tests for detecting clustering in multivariate Value-at-Risk (VaR) forecasts. First, we consider CUSUM-tests to detect first-order instationarities in the matrix of VaR-violations. Second, we propose χ 2-tests for detecting cross-sectional and serial dependence in the VaR-forecasts. Moreover, we combine our new backtests with a test of unconditional coverage to yield two new backtests of multivariate conditional coverage. In all cases, a bootstrap approximation is possible, but not mandatory in terms of empirical size and power.	en
dc.identifier.uri	http://hdl.handle.net/2003/33998
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-7210
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB 823;9/2015	en
dc.subject	Model Risk	en
dc.subject	Value-at-Risk	en
dc.subject	Multivariate Backtesting	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.title	Evaluating value-at-risk forecasts	en
dc.title.alternative	A new set of multivariate backtests	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access

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