Reinforcement learning for option pricing and hedging, a practical edge over black-scholes-merton model

Reinforcement learning for option pricing and hedging, a practical edge over black-scholes-merton model

This thesis explores two frameworks leveraging on modern Reinforcement Learning (RL) techniques for pricing and dynamic hedging of an option under practical market conditions such as transaction costs, trader risk-aversion, and stochastic volatility which the Black-Scholes-Merton’s (BSM) model fails...

Full description

Saved in:
Bibliographic Details
Main Author: Yang, Daniel
Other Authors: Bo An
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Computer and Information Science
Mathematical Sciences
Other
Online Access:https://hdl.handle.net/10356/175061
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-175061
record_format dspace
spelling sg-ntu-dr.10356-1750612024-04-19T15:41:40Z Reinforcement learning for option pricing and hedging, a practical edge over black-scholes-merton model Yang, Daniel Bo An School of Computer Science and Engineering boan@ntu.edu.sg Computer and Information Science Mathematical Sciences Other This thesis explores two frameworks leveraging on modern Reinforcement Learning (RL) techniques for pricing and dynamic hedging of an option under practical market conditions such as transaction costs, trader risk-aversion, and stochastic volatility which the Black-Scholes-Merton’s (BSM) model fails to consider. In this thesis, model-free methods of Q-learning and Deep Reinforcement Learning (DRL) are formulated under these real-world market settings with minimal market assumptions to dynamically hedge in an incomplete market where perfect Delta-hedging through the BSM model is not possible. Bachelor's degree 2024-04-19T00:36:22Z 2024-04-19T00:36:22Z 2024 Final Year Project (FYP) Yang, D. (2024). Reinforcement learning for option pricing and hedging, a practical edge over black-scholes-merton model. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175061 https://hdl.handle.net/10356/175061 en SCSE23-0062 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Mathematical Sciences
Other
spellingShingle Computer and Information Science
Mathematical Sciences
Other
Yang, Daniel
Reinforcement learning for option pricing and hedging, a practical edge over black-scholes-merton model
description This thesis explores two frameworks leveraging on modern Reinforcement Learning (RL) techniques for pricing and dynamic hedging of an option under practical market conditions such as transaction costs, trader risk-aversion, and stochastic volatility which the Black-Scholes-Merton’s (BSM) model fails to consider. In this thesis, model-free methods of Q-learning and Deep Reinforcement Learning (DRL) are formulated under these real-world market settings with minimal market assumptions to dynamically hedge in an incomplete market where perfect Delta-hedging through the BSM model is not possible.
author2 Bo An
author_facet Bo An
Yang, Daniel
format Final Year Project
author Yang, Daniel
author_sort Yang, Daniel
title Reinforcement learning for option pricing and hedging, a practical edge over black-scholes-merton model
title_short Reinforcement learning for option pricing and hedging, a practical edge over black-scholes-merton model
title_full Reinforcement learning for option pricing and hedging, a practical edge over black-scholes-merton model
title_fullStr Reinforcement learning for option pricing and hedging, a practical edge over black-scholes-merton model
title_full_unstemmed Reinforcement learning for option pricing and hedging, a practical edge over black-scholes-merton model
title_sort reinforcement learning for option pricing and hedging, a practical edge over black-scholes-merton model
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/175061
_version_ 1800916242768330752

Similar Items