Functional quantization-based stratified sampling

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dc.contributor.advisorMcWalter, Thomasen_ZA
dc.contributor.authorPlatts, Alexanderen_ZA
dc.date.accessioned2018-01-30T10:26:29Z
dc.date.available2018-01-30T10:26:29Z
dc.date.issued2017en_ZA
dc.description.abstractFunctional quantization-based stratified sampling is a method for variance reduction proposed by Corlay and Pagès (2015). This method requires the ability to both create functional quantizers and to sample Brownian paths from the strata defined by the quantizers. We show that product quantizers are a suitable approximation of an optimal quantizer for the formation of functional quantizers. The notion of functional stratification is then extended to options written on multiple stocks and American options priced using the Longstaff-Schwartz method. To illustrate the gains in performance we focus on geometric brownian motion (GBM), constant elasticity of variance (CEV) and constant elasticity of variance with stochastic volatility (CEV-SV) models. The pricing algorithm is used to price knock-in, knockout, autocall, call on the max and path dependent call on the max options.en_ZA
dc.identifier.apacitationPlatts, A. (2017). <i>Functional quantization-based stratified sampling</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science. Retrieved from http://hdl.handle.net/11427/27105en_ZA
dc.identifier.chicagocitationPlatts, Alexander. <i>"Functional quantization-based stratified sampling."</i> Thesis., University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science, 2017. http://hdl.handle.net/11427/27105en_ZA
dc.identifier.citationPlatts, A. 2017. Functional quantization-based stratified sampling. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Platts, Alexander AB - Functional quantization-based stratified sampling is a method for variance reduction proposed by Corlay and Pagès (2015). This method requires the ability to both create functional quantizers and to sample Brownian paths from the strata defined by the quantizers. We show that product quantizers are a suitable approximation of an optimal quantizer for the formation of functional quantizers. The notion of functional stratification is then extended to options written on multiple stocks and American options priced using the Longstaff-Schwartz method. To illustrate the gains in performance we focus on geometric brownian motion (GBM), constant elasticity of variance (CEV) and constant elasticity of variance with stochastic volatility (CEV-SV) models. The pricing algorithm is used to price knock-in, knockout, autocall, call on the max and path dependent call on the max options. DA - 2017 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2017 T1 - Functional quantization-based stratified sampling TI - Functional quantization-based stratified sampling UR - http://hdl.handle.net/11427/27105 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/27105
dc.identifier.vancouvercitationPlatts A. Functional quantization-based stratified sampling. [Thesis]. University of Cape Town ,Faculty of Commerce ,Division of Actuarial Science, 2017 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/27105en_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.titleFunctional quantization-based stratified samplingen_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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