Deep Hedging of basis risk

dc.contributor.advisorOuwehand, Peter
dc.contributor.authorAdewusi, Olatomiwa Ayooluwa
dc.date.accessioned2023-02-23T09:04:40Z
dc.date.available2023-02-23T09:04:40Z
dc.date.issued2022
dc.date.updated2023-02-20T12:09:58Z
dc.description.abstractBasis 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.
dc.identifier.apacitationAdewusi, O. A. (2022). <i>Deep Hedging of basis risk</i>. (). ,Faculty of Commerce ,Department of Finance and Tax. Retrieved from http://hdl.handle.net/11427/37002en_ZA
dc.identifier.chicagocitationAdewusi, Olatomiwa Ayooluwa. <i>"Deep Hedging of basis risk."</i> ., ,Faculty of Commerce ,Department of Finance and Tax, 2022. http://hdl.handle.net/11427/37002en_ZA
dc.identifier.citationAdewusi, O.A. 2022. Deep Hedging of basis risk. . ,Faculty of Commerce ,Department of Finance and Tax. http://hdl.handle.net/11427/37002en_ZA
dc.identifier.ris TY - Master Thesis AU - Adewusi, Olatomiwa Ayooluwa AB - 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. DA - 2022_ DB - OpenUCT DP - University of Cape Town KW - Mathematical Finance LK - https://open.uct.ac.za PY - 2022 T1 - Deep Hedging of basis risk TI - Deep Hedging of basis risk UR - http://hdl.handle.net/11427/37002 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/37002
dc.identifier.vancouvercitationAdewusi OA. Deep Hedging of basis risk. []. ,Faculty of Commerce ,Department of Finance and Tax, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37002en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Finance and Tax
dc.publisher.facultyFaculty of Commerce
dc.subjectMathematical Finance
dc.titleDeep Hedging of basis risk
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMPhil
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