Inflation modelling for long-term liability driven investments

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dc.contributor.advisorMollentz, Gregen_ZA
dc.contributor.advisorMcWalter, Tomen_ZA
dc.contributor.authorDe Kock, Justinen_ZA
dc.date.accessioned2016-04-05T11:42:31Z
dc.date.available2016-04-05T11:42:31Z
dc.date.issued2014en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractA regime-switching model allows a process to switch randomly between different regimes which have different parameter estimates. This study investigates the use of a two regime-switching model for inflation in South Africa as a means of determining a hedging strategy for inflation linked liabilities of a financial institution. Each regime is modeled using an autoregressive process with different parameters and the change in regimes is governed by a two state Markov chain. Once the parameters have been estimated, the predictive validity of the regime-switching process as a model for inflation in South Africa is tested and a hedging strategy is outlined for a set of inflation linked cash flows. The hedging strategy is to invest in inflation linked bonds, the number of which is determined through the use of a Rand-per-point methodology that is applied to the inflation linked cash flows and inflation linked bonds. Over the period from January 2008 to June 2013 this hedging strategy was shown to be profitable.en_ZA
dc.identifier.apacitationDe Kock, J. (2014). <i>Inflation modelling for long-term liability driven investments</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science. Retrieved from http://hdl.handle.net/11427/18602en_ZA
dc.identifier.chicagocitationDe Kock, Justin. <i>"Inflation modelling for long-term liability driven investments."</i> Thesis., University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science, 2014. http://hdl.handle.net/11427/18602en_ZA
dc.identifier.citationDe Kock, J. 2014. Inflation modelling for long-term liability driven investments. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - De Kock, Justin AB - A regime-switching model allows a process to switch randomly between different regimes which have different parameter estimates. This study investigates the use of a two regime-switching model for inflation in South Africa as a means of determining a hedging strategy for inflation linked liabilities of a financial institution. Each regime is modeled using an autoregressive process with different parameters and the change in regimes is governed by a two state Markov chain. Once the parameters have been estimated, the predictive validity of the regime-switching process as a model for inflation in South Africa is tested and a hedging strategy is outlined for a set of inflation linked cash flows. The hedging strategy is to invest in inflation linked bonds, the number of which is determined through the use of a Rand-per-point methodology that is applied to the inflation linked cash flows and inflation linked bonds. Over the period from January 2008 to June 2013 this hedging strategy was shown to be profitable. DA - 2014 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - Inflation modelling for long-term liability driven investments TI - Inflation modelling for long-term liability driven investments UR - http://hdl.handle.net/11427/18602 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/18602
dc.identifier.vancouvercitationDe Kock J. Inflation modelling for long-term liability driven investments. [Thesis]. University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science, 2014 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/18602en_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.otherActuarial Scienceen_ZA
dc.titleInflation modelling for long-term liability driven investmentsen_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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