Computational methods for various stochastic differential equation models in finance
This study develops efficient numerical methods for solving jumpdiffusion stochastic delay differential equations and stochastic differential equations with fractional order. In addition, two novel algorithms are developed for the estimation of parameters in the stochastic models. One of the algorit...
| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
Curtin University
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/247 |
| _version_ | 1848743324969598976 |
|---|---|
| author | Zhou, Yanli |
| author_facet | Zhou, Yanli |
| author_sort | Zhou, Yanli |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This study develops efficient numerical methods for solving jumpdiffusion stochastic delay differential equations and stochastic differential equations with fractional order. In addition, two novel algorithms are developed for the estimation of parameters in the stochastic models. One of the algorithms is based on the implementation of the Bayesian inference and the Markov Chain Monte Carlo method, while the other one is developed by using an implicit numerical scheme integrated with the particle swarm optimization. |
| first_indexed | 2025-11-14T05:43:46Z |
| format | Thesis |
| id | curtin-20.500.11937-247 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T05:43:46Z |
| publishDate | 2014 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-2472017-02-20T06:41:35Z Computational methods for various stochastic differential equation models in finance Zhou, Yanli This study develops efficient numerical methods for solving jumpdiffusion stochastic delay differential equations and stochastic differential equations with fractional order. In addition, two novel algorithms are developed for the estimation of parameters in the stochastic models. One of the algorithms is based on the implementation of the Bayesian inference and the Markov Chain Monte Carlo method, while the other one is developed by using an implicit numerical scheme integrated with the particle swarm optimization. 2014 Thesis http://hdl.handle.net/20.500.11937/247 en Curtin University fulltext |
| spellingShingle | Zhou, Yanli Computational methods for various stochastic differential equation models in finance |
| title | Computational methods for various stochastic differential equation models in finance |
| title_full | Computational methods for various stochastic differential equation models in finance |
| title_fullStr | Computational methods for various stochastic differential equation models in finance |
| title_full_unstemmed | Computational methods for various stochastic differential equation models in finance |
| title_short | Computational methods for various stochastic differential equation models in finance |
| title_sort | computational methods for various stochastic differential equation models in finance |
| url | http://hdl.handle.net/20.500.11937/247 |