Analyze option pricing methods, comparing FDM and Monte Carlo simulations, while exploring variance reduction techniques in the Black-Scholes model.

Discover theorems for reducing hedge error variance in option pricing. Explore strategies for optimal portfolio selection and enhanced risk management.

Explore innovative methods to enhance hedge error approximations in the Black-Scholes model using finite difference techniques and multivariate securities.

This research presents innovative methods to reduce hedge errors in the Black-Scholes model, enhancing the accuracy and efficiency of option pricing simulations

Finite difference methods approximate derivatives to simulate market dynamics, offering flexibility and accuracy for option pricing and hedging strategies.

This literature review examines classical and modern methods for hedge error approximation in the Black-Scholes model.

Explore how variance reduction techniques like importance sampling, antithetic variates, and control variates improve accuracy and efficiency in simulations.

Learn how the Black-Scholes model calculates European option prices using factors like volatility, strike price, and risk-free rates,.

Explore the training procedures for a DRL agent in hedging American put options using GBM and stochastic volatility models, plus Chebyshev interpolation for pr

This study outlines the methodology for implementing a deep reinforcement learning (DRL) agent for hedging American put options.

This study reviews prior research on reinforcement learning in option hedging, highlighting key methodologies and gaps in literature for American options.

Explore reinforcement learning (RL) and deep reinforcement learning (DRL) in financial hedging, focusing on their application in American put options.

Explore how deep reinforcement learning (DRL) using the DDPG method enhances hedging strategies for American put options.

This study explores using deep reinforcement learning, specifically the DDPG method, to hedge American put options.

A comprehensive list of sources cited in the study, covering foundational and contemporary literature on DRL and hedging American put options.

Supplementary data on final P&L statistics, option pricing results, and asset paths for testing in the DRL hedging study.

This study showcases how deep reinforcement learning agents effectively hedge American put options, outperforming traditional strategies in various scenarios.

This study evaluates DRL agents hedging options with market-calibrated stochastic volatility, comparing their performance against the BS Delta strategy.

Learn how a deep reinforcement learning agent performs in hedging American put options, comparing effectiveness across GBM and stochastic volatility scenarios.

Explore the testing procedures for DRL agents in hedging American put options, detailing asset pricing, exercise boundaries, and benchmark strategies.