A reinforcement learning framework for dynamic hedging in options markets: evidence from equity and ETFS options under different market regimes
Polimanti, Leonardo Maria (A.A. 2024/2025) A reinforcement learning framework for dynamic hedging in options markets: evidence from equity and ETFS options under different market regimes. Tesi di Laurea in Financial economics, Luiss Guido Carli, relatore Nicola Borri, pp. 167. [Master's Degree Thesis]
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Abstract/Index
Foundations of options contracts. Financial derivatives. Option contracts and their properties. Option trading strategies. Mathematical and probabilistic framework. Probabilistic foundations for finance. Stochastic differential equations (SDES). Partial differential equations (PDES). Mathematical tools in machine and reinforcement learning. Dynamic hedging and option pricing methods. Option pricing methods. Volatility concepts. Dynamic hedging. Reinforcement learning algorithms for hedging applications. Foundations of reinforcement learning. Deep learning and function approximation. Deep reinforcement learning algorithms. Theoretical application to dynamic hedging. Data description and empirical settings. Asset universe and data sources. Market regimes and sample segmentation. Risk-free rate data and term structure construction. Dataset construction. Option pricing and volatility specification. Implied volatility and Greeks computation. Descriptive overview of the final datasets. Empirical analysis: reinforcement learning for dynamic hedging. Empirical environment and hedging setup. Proximal policy optimization: empirical implementation. Experimental design and generalization strategy. Benchmark dynamic hedging strategies. Consolidated performance metrics. Robustness and sensitivity analysis.
References
Bibliografia: pp. 141-145.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Economics and Finance (LM-56) |
| Chair: | Financial economics |
| Thesis Supervisor: | Borri, Nicola |
| Thesis Co-Supervisor: | Biagini, Sara |
| Academic Year: | 2024/2025 |
| Session: | Extraordinary |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 18 Jun 2026 13:34 |
| Last Modified: | 18 Jun 2026 13:34 |
| URI: | https://tesi.luiss.it/id/eprint/46219 |
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