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Deep Hedging of basis risk
Basis risk arises when the writer of a contingent claim cannot trade in the underlying asset and must use a correlated proxy asset to hedge the contingent claim. Suppose the proxy asset is not perfectly correlated to the underlying. In that case, there is a risk that the hedge portfolio does not pre...
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| Format: | Thesis |
| Language: | English |
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Department of Finance and Tax
2023
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| _version_ | 1867613169157931008 |
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| access_status_str | Open Access |
| author | Adewusi, Olatomiwa Ayooluwa |
| author2 | Ouwehand, Peter |
| author_browse | Adewusi, Olatomiwa Ayooluwa Ouwehand, Peter |
| author_facet | Ouwehand, Peter Adewusi, Olatomiwa Ayooluwa |
| author_sort | Adewusi, Olatomiwa Ayooluwa |
| collection | Thesis |
| description | Basis risk arises when the writer of a contingent claim cannot trade in the underlying asset and must use a correlated proxy asset to hedge the contingent claim. Suppose the proxy asset is not perfectly correlated to the underlying. In that case, there is a risk that the hedge portfolio does not precisely track the contingent claim, which may lead to significant losses at maturity. There are several existing approaches to hedging and pricing of contingent claims in the presence of basis risk. The existing approaches considered in this dissertation are based on the quadratic and exponential utility functions. This dissertation compares these current approaches to a new policy that parameterises the hedge parameters as a recurrent neural network at each rebalancing date. This new approach is called Deep Hedging, and under this approach, the hedge parameters are determined in a model agnostic way. This is achieved using Long Short-Term Memory networks written in TensorFlow. This allows one to make the hedge parameters at each time point a function of current market data and previous hedging decisions. Deep Hedging is Greek-free and more easily allows for the incorporation of other market frictions, like transaction costs, compared to existing approaches. Lastly, we can find optimal hedging strategies under coherent risk metrics, like expected shortfall, using the Deep Hedging approach and given a price. By fixing the volatility and correlation parameters, Deep Hedging produces results that are comparable to the best existing strategies, in both complete and incomplete market settings, across a variety of moneyness levels. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/37002 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:52.071Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Department of Finance and Tax |
| publisherStr | Department of Finance and Tax |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/37002 Deep Hedging of basis risk Adewusi, Olatomiwa Ayooluwa Ouwehand, Peter Mathematical Finance Basis risk arises when the writer of a contingent claim cannot trade in the underlying asset and must use a correlated proxy asset to hedge the contingent claim. Suppose the proxy asset is not perfectly correlated to the underlying. In that case, there is a risk that the hedge portfolio does not precisely track the contingent claim, which may lead to significant losses at maturity. There are several existing approaches to hedging and pricing of contingent claims in the presence of basis risk. The existing approaches considered in this dissertation are based on the quadratic and exponential utility functions. This dissertation compares these current approaches to a new policy that parameterises the hedge parameters as a recurrent neural network at each rebalancing date. This new approach is called Deep Hedging, and under this approach, the hedge parameters are determined in a model agnostic way. This is achieved using Long Short-Term Memory networks written in TensorFlow. This allows one to make the hedge parameters at each time point a function of current market data and previous hedging decisions. Deep Hedging is Greek-free and more easily allows for the incorporation of other market frictions, like transaction costs, compared to existing approaches. Lastly, we can find optimal hedging strategies under coherent risk metrics, like expected shortfall, using the Deep Hedging approach and given a price. By fixing the volatility and correlation parameters, Deep Hedging produces results that are comparable to the best existing strategies, in both complete and incomplete market settings, across a variety of moneyness levels. 2023-02-23T09:04:40Z 2023-02-23T09:04:40Z 2022 2023-02-20T12:09:58Z Master Thesis Masters MPhil http://hdl.handle.net/11427/37002 eng application/pdf Department of Finance and Tax Faculty of Commerce |
| spellingShingle | Mathematical Finance Adewusi, Olatomiwa Ayooluwa Deep Hedging of basis risk |
| thesis_degree_str | Master's |
| title | Deep Hedging of basis risk |
| title_full | Deep Hedging of basis risk |
| title_fullStr | Deep Hedging of basis risk |
| title_full_unstemmed | Deep Hedging of basis risk |
| title_short | Deep Hedging of basis risk |
| title_sort | deep hedging of basis risk |
| topic | Mathematical Finance |
| url | http://hdl.handle.net/11427/37002 |
| work_keys_str_mv | AT adewusiolatomiwaayooluwa deephedgingofbasisrisk |