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Functional quantization-based stratified sampling

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...

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Main Author: Platts, Alexander
Other Authors: McWalter, Thomas
Format: Thesis
Language:English
Published: Division of Actuarial Science 2018
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access_status_str Open Access
author Platts, Alexander
author2 McWalter, Thomas
author_browse McWalter, Thomas
Platts, Alexander
author_facet McWalter, Thomas
Platts, Alexander
author_sort Platts, Alexander
collection Thesis
description 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.
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institution University of Cape Town (South Africa)
language eng
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license_str Not specified — see source repository
provenance_str_mv Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository
publishDate 2018
publishDateRange 2018
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spelling oai:open.uct.ac.za:11427/27105 Functional quantization-based stratified sampling Platts, Alexander McWalter, Thomas Mathematical Finance 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. 2018-01-30T10:26:29Z 2018-01-30T10:26:29Z 2017 Master Thesis Masters MPhil http://hdl.handle.net/11427/27105 eng application/pdf Division of Actuarial Science Faculty of Commerce University of Cape Town
spellingShingle Mathematical Finance
Platts, Alexander
Functional quantization-based stratified sampling
thesis_degree_str Master's
title Functional quantization-based stratified sampling
title_full Functional quantization-based stratified sampling
title_fullStr Functional quantization-based stratified sampling
title_full_unstemmed Functional quantization-based stratified sampling
title_short Functional quantization-based stratified sampling
title_sort functional quantization based stratified sampling
topic Mathematical Finance
url http://hdl.handle.net/11427/27105
work_keys_str_mv AT plattsalexander functionalquantizationbasedstratifiedsampling