Deep hedging of CVA
(2024)Department of Automatic Control
- Abstract
- Large financial institutions are vulnerable to numerous financial risks, necessitating robust regulatory frameworks to prevent crises such as those experienced in 2008. The Basel framework, devised by the Basel Committee on Banking Supervision, incorporates critical measures such as the credit valuation adjustment (CVA) to mitigate these risks. CVA fluctuates significantly based on market factors and counterparty conditions, these fluctuations need to be handled, and this is done through hedging. Hedging CVA is challenging due to its sensitivity to dynamic market conditions and the complexity of underlying assets, compounded by factors such as cross-gamma and wrong-way risk, which add significant complexity to effective risk management.... (More)
- Large financial institutions are vulnerable to numerous financial risks, necessitating robust regulatory frameworks to prevent crises such as those experienced in 2008. The Basel framework, devised by the Basel Committee on Banking Supervision, incorporates critical measures such as the credit valuation adjustment (CVA) to mitigate these risks. CVA fluctuates significantly based on market factors and counterparty conditions, these fluctuations need to be handled, and this is done through hedging. Hedging CVA is challenging due to its sensitivity to dynamic market conditions and the complexity of underlying assets, compounded by factors such as cross-gamma and wrong-way risk, which add significant complexity to effective risk management. This study explores the use of deep hedging, employing reinforcement learning to devise robust hedging strategies that navigate the complexities often associated with traditional analytic models. Through experimental simulations, this research compares the efficacy of traditional delta hedging with that of a reinforcement learning-based strategy, providing insights into their respective performances. The study evaluates two different market models, with the RL strategies showing promising results, particularly in the less complex model, highlighting the challenges of addressing high-dimensional problems. The findings establish a foundation for further research and demonstrate the potential of reinforcement learning in enhancing CVA hedging strategies. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9174022
- author
- Gummesson Atroshi, Oscar and Sibai, Osman
- supervisor
- organization
- year
- 2024
- type
- H3 - Professional qualifications (4 Years - )
- subject
- report number
- TFRT-6253
- other publication id
- 0280-5316
- language
- English
- id
- 9174022
- date added to LUP
- 2024-09-09 09:16:49
- date last changed
- 2024-09-09 09:16:49
@misc{9174022,
abstract = {{Large financial institutions are vulnerable to numerous financial risks, necessitating robust regulatory frameworks to prevent crises such as those experienced in 2008. The Basel framework, devised by the Basel Committee on Banking Supervision, incorporates critical measures such as the credit valuation adjustment (CVA) to mitigate these risks. CVA fluctuates significantly based on market factors and counterparty conditions, these fluctuations need to be handled, and this is done through hedging. Hedging CVA is challenging due to its sensitivity to dynamic market conditions and the complexity of underlying assets, compounded by factors such as cross-gamma and wrong-way risk, which add significant complexity to effective risk management. This study explores the use of deep hedging, employing reinforcement learning to devise robust hedging strategies that navigate the complexities often associated with traditional analytic models. Through experimental simulations, this research compares the efficacy of traditional delta hedging with that of a reinforcement learning-based strategy, providing insights into their respective performances. The study evaluates two different market models, with the RL strategies showing promising results, particularly in the less complex model, highlighting the challenges of addressing high-dimensional problems. The findings establish a foundation for further research and demonstrate the potential of reinforcement learning in enhancing CVA hedging strategies.}},
author = {{Gummesson Atroshi, Oscar and Sibai, Osman}},
language = {{eng}},
note = {{Student Paper}},
title = {{Deep hedging of CVA}},
year = {{2024}},
}