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dc.contributor.authorGençay, R.en_US
dc.contributor.authorSelçuk, F.en_US
dc.date.accessioned2015-07-28T11:57:20Z
dc.date.available2015-07-28T11:57:20Z
dc.date.issued2004en_US
dc.identifier.issn0169-2070
dc.identifier.urihttp://hdl.handle.net/11693/11297
dc.description.abstractIn this paper, we investigate the relative performance of Value-at-Risk (VaR) models with the daily stock market returns of nine different emerging markets. In addition to well-known modeling approaches, such as variance-covariance method and historical simulation, we study the extreme value theory (EVT) to generate VaR estimates and provide the tail forecasts of daily returns at the 0.999 percentile along with 95% confidence intervals for stress testing purposes. The results indicate that EVT-based VaR estimates are more accurate at higher quantiles. According to estimated Generalized Pareto Distribution parameters, certain moments of the return distributions do not exist in some countries. In addition, the daily return distributions have different moment properties at their right and left tails. Therefore, risk and reward are not equally likely in these economies. (C) 2004 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.en_US
dc.language.isoEnglishen_US
dc.source.titleInternational Journal of Forecastingen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.ijforecast.2003.09.005en_US
dc.subjectValue-at-Risken_US
dc.subjectFinancial risk managementen_US
dc.subjectExtreme value theoryen_US
dc.subjectNonlinear tail forecastsen_US
dc.titleExtreme value theory and Value-at-Risk: relative performance in emerging marketsen_US
dc.typeArticleen_US
dc.departmentDepartment of Economicsen_US
dc.citation.spage287en_US
dc.citation.epage303en_US
dc.citation.volumeNumber20en_US
dc.citation.issueNumber2en_US
dc.identifier.doi10.1016/j.ijforecast.2003.09.005en_US
dc.publisherElsevier BVen_US
dc.identifier.eissn1872-8200


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