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The double Heston model via filtering methods

Thesis (MSc)--Stellenbosch University, 2016

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Main Author: Namundjebo, Elia N
Other Authors: Sanders, J. W.
Format: Thesis
Language:en_ZA
Published: Stellenbosch : Stellenbosch University 2016
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access_status_str Open Access
author Namundjebo, Elia N
author2 Sanders, J. W.
author_browse Namundjebo, Elia N
Sanders, J. W.
author_facet Sanders, J. W.
Namundjebo, Elia N
author_sort Namundjebo, Elia N
collection Thesis
dc_rights_str_mv Stellenbosch University
description Thesis (MSc)--Stellenbosch University, 2016
format Thesis
id oai:scholar.sun.ac.za:10019.1/100371
institution Stellenbosch University (South Africa)
language en_ZA
last_indexed 2026-06-10T12:47:16.314Z
license_str Other — see source repository
provenance_str_mv Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository
publishDate 2016
publishDateRange 2016
publishDateSort 2016
publisher Stellenbosch : Stellenbosch University
publisherStr Stellenbosch : Stellenbosch University
record_format dspace
source_str SUNScholar — Stellenbosch University Repository
spelling oai:scholar.sun.ac.za:10019.1/100371 The double Heston model via filtering methods Namundjebo, Elia N Sanders, J. W. Stellenbosch University. Faculty of Science. Department of Mathematical Sciences Mathematical finance -- Stochastic volatility model Mathematical finance -- Double Heston model Mathematical finance -- Non-linear filtering Maximum likelihood estimation UCTD Finance -- Mathematical models Thesis (MSc)--Stellenbosch University, 2016 ENGLISH ABSTRACT : Stochastic volatility models are well-known for their ability to generate a volatility smile for financial securities. The development of the stochastic volatility models followed shortly after the crash of 1987 which violates the Black-Scholes model which has constant volatility. In this study we introduce non-linear filtering methods to estimate the implied volatilities of the Double Heston model. We compare our results to the Standard Heston model. The non-linear filtering methods used are the extended Kalman filter, the unscented Kalman filter and the particle filter. We combine the filtering methods together with the maximum likelihood estimation method to estimate the model's hidden parameters. Our numerical results show that the Double Heston model ts the market implied volatilities better than the Standard Heston model. The particle lter also performs better than the other two filters. AFRIKAANSE OPSOMMING : Stogastiese wisselvalligheid modelle is goed bekend vir hul vermoë om'n wisselvalligheid glimlag vir finansiële sekuriteite te genereer. Die ontwikkeling van die stogastiese wisselvalligheid modelle het gevolg kort nadat die ongeluk van 1987 wat die Black-Scholes model wat konstant wisselvalligheid oortree het. In hierdie studie stel ons nie-lineêre filter metodes voor om die ge lmpliseerde wisselings in die Double Heston Model te skat. Ons vergelyk ons resultate aan die Standard Heston model. Die nie-lineêre lter metodes wat gebruik word is die uitgebreide Kalman filter, die reuklose Kalman filter en die deeltjies fillter. Ons kombineer die filter metodes saam met die maksimum annneemlikheidsberaming metode om verborge parameters van die model te skat. Ons numeriese resultate dui daarop dat die Double Heston model pas die mark geïmpliseerde volatiliteit en beter as die Standard Heston model. Die deeltjie filter presteer ook beter as die ander twee fillters. 2016-12-22T13:45:50Z 2016-12-22T13:45:50Z 2016-12 Thesis http://hdl.handle.net/10019.1/100371 en_ZA Stellenbosch University vi, 74 pages application/pdf Stellenbosch : Stellenbosch University
spellingShingle Mathematical finance -- Stochastic volatility model
Mathematical finance -- Double Heston model
Mathematical finance -- Non-linear filtering
Maximum likelihood estimation
UCTD
Finance -- Mathematical models
Namundjebo, Elia N
The double Heston model via filtering methods
title The double Heston model via filtering methods
title_full The double Heston model via filtering methods
title_fullStr The double Heston model via filtering methods
title_full_unstemmed The double Heston model via filtering methods
title_short The double Heston model via filtering methods
title_sort double heston model via filtering methods
topic Mathematical finance -- Stochastic volatility model
Mathematical finance -- Double Heston model
Mathematical finance -- Non-linear filtering
Maximum likelihood estimation
UCTD
Finance -- Mathematical models
url http://hdl.handle.net/10019.1/100371
work_keys_str_mv AT namundjeboelian thedoublehestonmodelviafilteringmethods
AT namundjeboelian doublehestonmodelviafilteringmethods