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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Protective Closing Strategy for Option Selling via Deep Reinforcement Learning |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | Selling put options can be lucrative; however, the returns tend to exhibit a strong negative skewness. Moreover, the seller may have liquidity issues during the holding period, especially when margin requirements become too large. Existing hedging techniques often overlook potential liquidity problems during the holding period, focusing solely on terminal losses. To address this limitation, we present a novel risk management approach by reformulating the closing time of the short position as an optimal stopping problem. To find the solutions, we decompose the holding period into a sequence of binary stopping decisions, which naturally fit into the reinforcement learning framework. Multiple deep reinforcement learning algorithms, namely Deep Q-Learning, Rainbow, and Synchronous Advantage Actor-Critic, are employed to identify the optimal times for closing the position. Our training framework introduces a new reward function that enables the agents to maximize each option’s profit and enhance its Sharpe ratio. In a simulated environment with nontrivial optimal stopping solutions, we demonstrate the e↵ectiveness of the algorithms and our training setup. Furthermore, we apply these algorithms to market data; specifically, SPY put option data from 2005 to 2022. During this analysis, we encounter a significant imbalance in the training data between paths with negative and positive returns, making it challenging for the algorithms to learn an optimal solution. Consequently, we propose several approaches to tackle this issue in future research. Overall, our work presents a promising approach to address liquidity concerns during option selling strategies, and our findings contribute to the advancement of reinforcement learning techniques in the financial domain. |
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