Estimating dynamic affine term structure models

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dc.contributor.advisorOuwehand, Peteren_ZA
dc.contributor.advisorMcWalter, Thomasen_ZA
dc.contributor.authorPitsillis, Zachry Stevenen_ZA
dc.date.accessioned2015-12-09T14:44:02Z
dc.date.available2015-12-09T14:44:02Z
dc.date.issued2015en_ZA
dc.description.abstractDuffee and Stanton (2012) demonstrated some pointed problems in estimating affine term structure models when the price of risk is dynamic, that is, risk factor dependent. The risk neutral parameters are estimated with precision, while the price of risk parameters are not. For the Gaussian models they investigated, these problems are replicated and are shown to stem from a lack of curvature in the log-likelihood function. This geometric issue for identifying the maximum of an essentially horizontal log-likelihood has statistical meaning. The Fisher information for the price of risk parameters is multiple orders of magnitude smaller than that of the risk neutral parameters. Prompted by the recent results of Christoffersen et al. (2014) a remedy to the lack of curvature is attempted. An unscented Kalman filter is used to estimate models where the observations are portfolios of FRAs, Swaps and Zero Coupon Bond Options. While the unscented Kalman filter performs admirably in identifying the unobserved risk factor processes, there is little improvement in the Fisher information.en_ZA
dc.identifier.apacitationPitsillis, Z. S. (2015). <i>Estimating dynamic affine term structure models</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science. Retrieved from http://hdl.handle.net/11427/15731en_ZA
dc.identifier.chicagocitationPitsillis, Zachry Steven. <i>"Estimating dynamic affine term structure models."</i> Thesis., University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science, 2015. http://hdl.handle.net/11427/15731en_ZA
dc.identifier.citationPitsillis, Z. 2015. Estimating dynamic affine term structure models. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Pitsillis, Zachry Steven AB - Duffee and Stanton (2012) demonstrated some pointed problems in estimating affine term structure models when the price of risk is dynamic, that is, risk factor dependent. The risk neutral parameters are estimated with precision, while the price of risk parameters are not. For the Gaussian models they investigated, these problems are replicated and are shown to stem from a lack of curvature in the log-likelihood function. This geometric issue for identifying the maximum of an essentially horizontal log-likelihood has statistical meaning. The Fisher information for the price of risk parameters is multiple orders of magnitude smaller than that of the risk neutral parameters. Prompted by the recent results of Christoffersen et al. (2014) a remedy to the lack of curvature is attempted. An unscented Kalman filter is used to estimate models where the observations are portfolios of FRAs, Swaps and Zero Coupon Bond Options. While the unscented Kalman filter performs admirably in identifying the unobserved risk factor processes, there is little improvement in the Fisher information. DA - 2015 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - Estimating dynamic affine term structure models TI - Estimating dynamic affine term structure models UR - http://hdl.handle.net/11427/15731 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/15731
dc.identifier.vancouvercitationPitsillis ZS. Estimating dynamic affine term structure models. [Thesis]. University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science, 2015 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/15731en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDivision of Actuarial Scienceen_ZA
dc.publisher.facultyFaculty of Commerceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherMathematical Financeen_ZA
dc.titleEstimating dynamic affine term structure modelsen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMPhilen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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