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Monte Carlo methods for the estimation of value-at-risk and related risk measures
Nested Monte Carlo is a computationally expensive exercise. The main contributions we present in this thesis are the formulation of efficient algorithms to perform nested Monte Carlo for the estimation of Value-at-Risk and Expected-Tail-Loss. The algorithms are designed to take advantage of multipro...
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| Format: | Thesis |
| Language: | English |
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Division of Actuarial Science
2015
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| _version_ | 1867613693784621056 |
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| access_status_str | Open Access |
| author | Marks, Dean |
| author2 | Becker, Ronald |
| author_browse | Becker, Ronald Marks, Dean |
| author_facet | Becker, Ronald Marks, Dean |
| author_sort | Marks, Dean |
| collection | Thesis |
| description | Nested Monte Carlo is a computationally expensive exercise. The main contributions we present in this thesis are the formulation of efficient algorithms to perform nested Monte Carlo for the estimation of Value-at-Risk and Expected-Tail-Loss. The algorithms are designed to take advantage of multiprocessing computer architecture by performing computational tasks in parallel. Through numerical experiments we show that our algorithms can improve efficiency in the sense of reducing mean-squared error. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/10966 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:40:12.731Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| 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:11427/10966 Monte Carlo methods for the estimation of value-at-risk and related risk measures Marks, Dean Becker, Ronald Mathematical Finance Nested Monte Carlo is a computationally expensive exercise. The main contributions we present in this thesis are the formulation of efficient algorithms to perform nested Monte Carlo for the estimation of Value-at-Risk and Expected-Tail-Loss. The algorithms are designed to take advantage of multiprocessing computer architecture by performing computational tasks in parallel. Through numerical experiments we show that our algorithms can improve efficiency in the sense of reducing mean-squared error. 2015-01-02T09:06:08Z 2015-01-02T09:06:08Z 2011 Master Thesis Masters MPhil http://hdl.handle.net/11427/10966 eng application/pdf Division of Actuarial Science Faculty of Commerce University of Cape Town |
| spellingShingle | Mathematical Finance Marks, Dean Monte Carlo methods for the estimation of value-at-risk and related risk measures |
| thesis_degree_str | Master's |
| title | Monte Carlo methods for the estimation of value-at-risk and related risk measures |
| title_full | Monte Carlo methods for the estimation of value-at-risk and related risk measures |
| title_fullStr | Monte Carlo methods for the estimation of value-at-risk and related risk measures |
| title_full_unstemmed | Monte Carlo methods for the estimation of value-at-risk and related risk measures |
| title_short | Monte Carlo methods for the estimation of value-at-risk and related risk measures |
| title_sort | monte carlo methods for the estimation of value at risk and related risk measures |
| topic | Mathematical Finance |
| url | http://hdl.handle.net/11427/10966 |
| work_keys_str_mv | AT marksdean montecarlomethodsfortheestimationofvalueatriskandrelatedriskmeasures |