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Article: On Bayesian value at risk: from linear to non-linear portfolios

TitleOn Bayesian value at risk: from linear to non-linear portfolios
Authors
KeywordsBayesian Method
Gerber-Shiu's Model
Leptokurtic Effect
Model Risk
Non-Linear Portfolios
Subjective Var
Issue Date2004
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=1387-2834
Citation
Asia-Pacific Financial Markets, 2004, v. 11 n. 2, p. 161-184 How to Cite?
AbstractThis paper proposes the use of Bayesian approach to implement Value at Risk (VaR) model for both linear and non-linear portfolios. The Bayesian approach provides risk traders with the flexibility of adjusting their VaR models according to their subjective views. First, we deal with the case of linear portfolios. By imposing the conjugate-prior assumptions, a closed-form expression for the Bayesian VaR is obtained. The Bayesian VaR model can also be adjusted in order to deal with the ageing effect of the past data. By adopting Gerber-Shiu's option-pricing model, our Bayesian VaR model can also be applied to deal with non-linear portfolios of derivatives. We obtain an exact formula for the Bayesian VaR in the case of a single European call option. We adopt the method of back-testing to compare the non-adjusted and adjusted Bayesian VaR models with their corresponding classica' counterparts in both linear and non-linear cases. © Springer 2006.
Persistent Identifierhttp://hdl.handle.net/10722/172426
ISSN
2015 SCImago Journal Rankings: 0.190
References

 

DC FieldValueLanguage
dc.contributor.authorSiu, TKen_US
dc.contributor.authorTong, Hen_US
dc.contributor.authorYang, Hen_US
dc.date.accessioned2012-10-30T06:22:27Z-
dc.date.available2012-10-30T06:22:27Z-
dc.date.issued2004en_US
dc.identifier.citationAsia-Pacific Financial Markets, 2004, v. 11 n. 2, p. 161-184en_US
dc.identifier.issn1387-2834en_US
dc.identifier.urihttp://hdl.handle.net/10722/172426-
dc.description.abstractThis paper proposes the use of Bayesian approach to implement Value at Risk (VaR) model for both linear and non-linear portfolios. The Bayesian approach provides risk traders with the flexibility of adjusting their VaR models according to their subjective views. First, we deal with the case of linear portfolios. By imposing the conjugate-prior assumptions, a closed-form expression for the Bayesian VaR is obtained. The Bayesian VaR model can also be adjusted in order to deal with the ageing effect of the past data. By adopting Gerber-Shiu's option-pricing model, our Bayesian VaR model can also be applied to deal with non-linear portfolios of derivatives. We obtain an exact formula for the Bayesian VaR in the case of a single European call option. We adopt the method of back-testing to compare the non-adjusted and adjusted Bayesian VaR models with their corresponding classica' counterparts in both linear and non-linear cases. © Springer 2006.en_US
dc.languageengen_US
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=1387-2834en_US
dc.relation.ispartofAsia-Pacific Financial Marketsen_US
dc.subjectBayesian Methoden_US
dc.subjectGerber-Shiu's Modelen_US
dc.subjectLeptokurtic Effecten_US
dc.subjectModel Risken_US
dc.subjectNon-Linear Portfoliosen_US
dc.subjectSubjective Varen_US
dc.titleOn Bayesian value at risk: from linear to non-linear portfoliosen_US
dc.typeArticleen_US
dc.identifier.emailYang, H: hlyang@hku.hken_US
dc.identifier.authorityYang, H=rp00826en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/s10690-006-9008-7en_US
dc.identifier.scopuseid_2-s2.0-33749501719en_US
dc.identifier.hkuros118247-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33749501719&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume11en_US
dc.identifier.issue2en_US
dc.identifier.spage161en_US
dc.identifier.epage184en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridSiu, TK=8655758200en_US
dc.identifier.scopusauthoridTong, H=7201359749en_US
dc.identifier.scopusauthoridYang, H=7406559537en_US

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