Full metadata record
DC FieldValueLanguage
dc.contributor.authorSun, Edward W.en_US
dc.contributor.authorWang, Yu-Jenen_US
dc.contributor.authorYu, Min-Tehen_US
dc.date.accessioned2019-04-02T05:58:43Z-
dc.date.available2019-04-02T05:58:43Z-
dc.date.issued2018-08-01en_US
dc.identifier.issn0927-7099en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s10614-017-9708-2en_US
dc.identifier.urihttp://hdl.handle.net/11536/148006-
dc.description.abstractPortfolio management and integrated risk management are more commonly applied toward enterprise risk management, requiring multivariate risk measures that capture the dependence among many risk factors. In this paper we propose the non-parametric estimator for multivariate value at risk (MVaR) and multivariate average value at risk (MAVaR) based on the multivariate geometric quantile approach and derive the symptotic properties of the proposed estimators for MVaR. We also present their performances under both simulated data and high-frequency financial data from the New York Stock Exchange. In addition, we compare our method with the delta normal approach and order statistics approach. The overall empirical results confirm that the multivariate geometric quantile approach significantly improves the risk management performance of MVaR and MAVaR.en_US
dc.language.isoen_USen_US
dc.subjectMultivariate value at risken_US
dc.subjectMultivariate average value at risken_US
dc.subjectMultivariate quantile regressionen_US
dc.subjectAsymptotics varianceen_US
dc.subjectM-estimationen_US
dc.titleIntegrated Portfolio Risk Measure: Estimation and Asymptotics of Multivariate Geometric Quantilesen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s10614-017-9708-2en_US
dc.identifier.journalCOMPUTATIONAL ECONOMICSen_US
dc.citation.volume52en_US
dc.citation.spage627en_US
dc.citation.epage652en_US
dc.contributor.department管理學院zh_TW
dc.contributor.departmentCollege of Managementen_US
dc.identifier.wosnumberWOS:000441531500015en_US
dc.citation.woscount1en_US
Appears in Collections:Articles