  This paper contributes to the existing literature on hedging American options
with Deep Reinforcement Learning (DRL). The study first investigates
hyperparameter impact on hedging performance, considering learning rates,
training episodes, neural network architectures, training steps, and
transaction cost penalty functions. Results highlight the importance of
avoiding certain combinations, such as high learning rates with a high number
of training episodes or low learning rates with few training episodes and
emphasize the significance of utilizing moderate values for optimal outcomes.
Additionally, the paper warns against excessive training steps to prevent
instability and demonstrates the superiority of a quadratic transaction cost
penalty function over a linear version. This study then expands upon the work
of Pickard et al. (2024), who utilize a Chebyshev interpolation option pricing
method to train DRL agents with market calibrated stochastic volatility models.
While the results of Pickard et al. (2024) showed that these DRL agents achieve
satisfactory performance on empirical asset paths, this study introduces a
novel approach where new agents at weekly intervals to newly calibrated
stochastic volatility models. Results show DRL agents re-trained using weekly
market data surpass the performance of those trained solely on the sale date.
Furthermore, the paper demonstrates that both single-train and weekly-train DRL
agents outperform the Black-Scholes Delta method at transaction costs of 1% and
3%. This practical relevance suggests that practitioners can leverage readily
available market data to train DRL agents for effective hedging of options in
their portfolios.
