Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/9612
DC FieldValueLanguage
dc.contributor.authorAndreou, Panayiotis-
dc.contributor.authorCharalambous, Chris-
dc.contributor.authorMartzoukos, Spiros H.-
dc.contributor.otherAνδρέου, Παναγιώτης-
dc.date.accessioned2017-02-13T10:50:16Z-
dc.date.available2017-02-13T10:50:16Z-
dc.date.issued2014-04-01-
dc.identifier.citationReview of Quantitative Finance and Accounting, 2014, vol. 42, no. 3, pp. 373-397en_US
dc.identifier.issn15737179-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/9612-
dc.description.abstractThis study examines several alternative symmetric and asymmetric model specifications of regression-based deterministic volatility models to identify the one that best characterizes the implied volatility functions of S&P 500 Index options in the period 1996-2009. We find that estimating the models with nonlinear least squares, instead of ordinary least squares, always results in lower pricing errors in both in- and out-of-sample comparisons. In-sample, asymmetric models of the moneyness ratio estimated separately on calls and puts provide the overall best performance. However, separating calls from puts violates the put-call-parity and leads to severe model mis-specification problems. Out-of-sample, symmetric models that use the logarithmic transformation of the strike price are the overall best ones. The lowest out-of-sample pricing errors are observed when implied volatility models are estimated consistently to the put-call-parity using the joint data set of out-of-the-money options. The out-of-sample pricing performance of the overall best model is shown to be resilient to extreme market conditions and compares quite favorably with continuous-time option pricing models that admit stochastic volatility and random jump risk factors.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofReview of Quantitative Finance and Accountingen_US
dc.rights© Springer Natureen_US
dc.subjectDeterministic volatility functionsen_US
dc.subjectImplied volatility forecastingen_US
dc.subjectModel selectionen_US
dc.subjectOption pricingen_US
dc.subjectStochastic volatilityen_US
dc.titleAssessing the performance of symmetric and asymmetric implied volatility functionsen_US
dc.typeArticleen_US
dc.doi10.1007/s11156-013-0346-zen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationDurham Universityen_US
dc.collaborationUniversity of Cyprusen_US
dc.subject.categoryEconomics and Businessen_US
dc.journalsSubscriptionen_US
dc.countryCyprusen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldSocial Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1007/s11156-013-0346-zen_US
dc.relation.issue3en_US
dc.relation.volume42en_US
cut.common.academicyear2013-2014en_US
dc.identifier.spage373en_US
dc.identifier.epage397en_US
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.openairetypearticle-
crisitem.author.deptDepartment of Finance, Accounting and Management Science-
crisitem.author.facultyFaculty of Tourism Management, Hospitality and Entrepreneurship-
crisitem.author.orcid0000-0001-5742-0311-
crisitem.author.parentorgFaculty of Management and Economics-
crisitem.journal.journalissn1573-7179-
crisitem.journal.publisherSpringer Nature-
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