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dc.contributor.authorŞener, Emrah
dc.contributor.authorBaronyan, Sayat
dc.contributor.authorMengütürk, L. A.
dc.date.accessioned2014-11-24T08:25:30Z
dc.date.available2014-11-24T08:25:30Z
dc.date.issued2012
dc.identifier.issn0169-2070
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0169207012000027
dc.identifier.urihttp://hdl.handle.net/10679/662
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractWe introduce a ranking model and a complementary predictive ability test statistic to investigate the forecasting performances of different Value at Risk (VaR) methods, without specifying a fixed benchmark method. The period including the recent credit crisis offers a unique laboratory for the analysis of the relative successes of different VaR methods when used in both emerging and developed markets. The proposed ranking model aims to form a unified framework which penalizes not only the magnitudes of errors between realized and predicted losses, but also the autocorrelation between the errors. The model also penalizes excessive capital allocations. In this respect, the ranking model seeks for VaR methods which can capture the delicate balance between the minimum governmental regulations for financial sustainability, and cost-efficient risk management for economic vitality. As a complimentary statistical tool for the ranking model, we introduce a predictive ability test which does not require the selection of a benchmark method. This statistic, which compares all methods simultaneously, is an alternative to existing predictive ability tests, which compare forecasting methods two at a time. We test and rank twelve different popular VaR methods on the equity indices of eleven emerging and seven developed markets. According to the ranking model and the predictive ability test, our empirical findings suggest that asymmetric methods, such as CAViaR Asymmetric and EGARCH, generate the best performing VaR forecasts. This indicates that the performance of VaR methods does not depend entirely on whether they are parametric, non-parametric, semi-parametric or hybrid; but rather on whether they can model the asymmetry of the underlying data effectively or not.en_US
dc.description.sponsorshipCapital Market Board of Turkey
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofInternational Journal of Forecasting
dc.rightsrestrictedAccess
dc.titleRanking the predictive performances of value-at-risk estimation methodsen_US
dc.typeArticleen_US
dc.peerreviewedyesen_US
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID201658
dc.contributor.authorID150427
dc.contributor.ozuauthorŞener, Emrah
dc.contributor.ozuauthorBaronyan, Sayat
dc.identifier.volume28
dc.identifier.issue4
dc.identifier.startpage849
dc.identifier.endpage873
dc.identifier.wosWOS:000308904200011
dc.identifier.doi10.1016/j.ijforecast.2011.10.002
dc.subject.keywordsValue at risken_US
dc.subject.keywordsPredictive ability testen_US
dc.subject.keywordsEGARCHen_US
dc.subject.keywordsCAViaR asymmetricen_US
dc.identifier.scopusSCOPUS:2-s2.0-84865148424
dc.contributor.authorMale2


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