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Parameter learning with particle filters
Common applications of asset-pricing models in practice rely on recalibrating model parameters periodically for effective risk management. Yet, these model parameters are often assumed to be constant over time, thereby countering the notion of readjusting these values. A possible solution to this pr...
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| Format: | Thesis |
| Language: | English |
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Division of Actuarial Science
2021
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| _version_ | 1867613209163202560 |
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| access_status_str | Open Access |
| author | Pather, Vegan |
| author2 | Rudd, Ralph |
| author_browse | Pather, Vegan Rudd, Ralph |
| author_facet | Rudd, Ralph Pather, Vegan |
| author_sort | Pather, Vegan |
| collection | Thesis |
| description | Common applications of asset-pricing models in practice rely on recalibrating model parameters periodically for effective risk management. Yet, these model parameters are often assumed to be constant over time, thereby countering the notion of readjusting these values. A possible solution to this problem is to recalibrate at times where observed market prices cannot realistically match model prices based on parameter values at those times. This dissertation aims to test the effectiveness of a possible algorithm which can be used in optimally identifying such times. An overview is provided of the recently proposed particle filter with accelerated adaptation which has demonstrated rapid time detection for changes in parameter values and has been applied to regime-shifting and stochastic volatility models. Numerical and graphical evidence of parameter and volatility estimation will be provided under regime-shifting parameters for the Heston (1993) stochastic volatility model. The filter demonstrates rapid adaptation in estimating parameter values and accurate estimation of the volatility process. Furthermore, we provide a discussion for possible extensions towards a metric for optimal recalibration times. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/32913 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:29.432Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Division of Actuarial Science |
| publisherStr | Division of Actuarial Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/32913 Parameter learning with particle filters Pather, Vegan Rudd, Ralph Soane, Andrew Mathematical Finance Common applications of asset-pricing models in practice rely on recalibrating model parameters periodically for effective risk management. Yet, these model parameters are often assumed to be constant over time, thereby countering the notion of readjusting these values. A possible solution to this problem is to recalibrate at times where observed market prices cannot realistically match model prices based on parameter values at those times. This dissertation aims to test the effectiveness of a possible algorithm which can be used in optimally identifying such times. An overview is provided of the recently proposed particle filter with accelerated adaptation which has demonstrated rapid time detection for changes in parameter values and has been applied to regime-shifting and stochastic volatility models. Numerical and graphical evidence of parameter and volatility estimation will be provided under regime-shifting parameters for the Heston (1993) stochastic volatility model. The filter demonstrates rapid adaptation in estimating parameter values and accurate estimation of the volatility process. Furthermore, we provide a discussion for possible extensions towards a metric for optimal recalibration times. 2021-02-20T20:22:42Z 2021-02-20T20:22:42Z 2020 2021-02-20T20:22:13Z Master Thesis Masters MPhil http://hdl.handle.net/11427/32913 eng application/pdf Division of Actuarial Science Faculty of Commerce |
| spellingShingle | Mathematical Finance Pather, Vegan Parameter learning with particle filters |
| thesis_degree_str | Master's |
| title | Parameter learning with particle filters |
| title_full | Parameter learning with particle filters |
| title_fullStr | Parameter learning with particle filters |
| title_full_unstemmed | Parameter learning with particle filters |
| title_short | Parameter learning with particle filters |
| title_sort | parameter learning with particle filters |
| topic | Mathematical Finance |
| url | http://hdl.handle.net/11427/32913 |
| work_keys_str_mv | AT pathervegan parameterlearningwithparticlefilters |