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Neural network libor market model for pricing and hedging interest rate derivatives
In this dissertation, we will introduce a new formulation of variational auto-encoders in order to generate the data we require. Our variational auto-encoder is based on data generation principles from elementary probability i.e. finding the inverse cumulative distribution function and using uniform...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2022
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| _version_ | 1867613261349781504 |
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| access_status_str | Open Access |
| author | Robbertze, Yuri |
| author2 | Mavuso, Melusi |
| author_browse | Mavuso, Melusi Robbertze, Yuri |
| author_facet | Mavuso, Melusi Robbertze, Yuri |
| author_sort | Robbertze, Yuri |
| collection | Thesis |
| description | In this dissertation, we will introduce a new formulation of variational auto-encoders in order to generate the data we require. Our variational auto-encoder is based on data generation principles from elementary probability i.e. finding the inverse cumulative distribution function and using uniform inputs to generate samples from the distribution. Like all autoencoders, the goal is to reduce the dimensionality in the kernel and use this to describe the data features in the generation. Our formulation will use a kernel which transforms the outputs of the encoder into multi-dimensional uniformly distributed variables, which in turn will learn the cumulative distribution function (in the case of a one dimensional latent space) or the relationship of variables to copula input uniforms (in the case of a multi-dimensional latent space). The decoder will then train to learn the inverse of the encoder and this will then be used to generate data. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/36545 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:19.547Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/36545 Neural network libor market model for pricing and hedging interest rate derivatives Robbertze, Yuri Mavuso, Melusi Statistical Sciences In this dissertation, we will introduce a new formulation of variational auto-encoders in order to generate the data we require. Our variational auto-encoder is based on data generation principles from elementary probability i.e. finding the inverse cumulative distribution function and using uniform inputs to generate samples from the distribution. Like all autoencoders, the goal is to reduce the dimensionality in the kernel and use this to describe the data features in the generation. Our formulation will use a kernel which transforms the outputs of the encoder into multi-dimensional uniformly distributed variables, which in turn will learn the cumulative distribution function (in the case of a one dimensional latent space) or the relationship of variables to copula input uniforms (in the case of a multi-dimensional latent space). The decoder will then train to learn the inverse of the encoder and this will then be used to generate data. 2022-06-27T20:22:24Z 2022-06-27T20:22:24Z 2022 2022-06-27T18:34:08Z Master Thesis Masters MSc http://hdl.handle.net/11427/36545 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Statistical Sciences Robbertze, Yuri Neural network libor market model for pricing and hedging interest rate derivatives |
| thesis_degree_str | Master's |
| title | Neural network libor market model for pricing and hedging interest rate derivatives |
| title_full | Neural network libor market model for pricing and hedging interest rate derivatives |
| title_fullStr | Neural network libor market model for pricing and hedging interest rate derivatives |
| title_full_unstemmed | Neural network libor market model for pricing and hedging interest rate derivatives |
| title_short | Neural network libor market model for pricing and hedging interest rate derivatives |
| title_sort | neural network libor market model for pricing and hedging interest rate derivatives |
| topic | Statistical Sciences |
| url | http://hdl.handle.net/11427/36545 |
| work_keys_str_mv | AT robbertzeyuri neuralnetworklibormarketmodelforpricingandhedginginterestratederivatives |