  We propose the Chiarella-Heston model, a new agent-based model for improving
the effectiveness of deep hedging strategies. This model includes momentum
traders, fundamental traders, and volatility traders. The volatility traders
participate in the market by innovatively following a Heston-style volatility
signal. The proposed model generalises both the extended Chiarella model and
the Heston stochastic volatility model, and is calibrated to reproduce as many
empirical stylized facts as possible. According to the stylised facts distance
metric, the proposed model is able to reproduce more realistic financial time
series than three baseline models: the extended Chiarella model, the Heston
model, and the Geometric Brownian Motion. The proposed model is further
validated by the Generalized Subtracted L-divergence metric. With the proposed
Chiarella-Heston model, we generate a training dataset to train a deep hedging
agent for optimal hedging strategies under various transaction cost levels. The
deep hedging agent employs the Deep Deterministic Policy Gradient algorithm and
is trained to maximize profits and minimize risks. Our testing results reveal
that the deep hedging agent, trained with data generated by our proposed model,
outperforms the baseline in most transaction cost levels. Furthermore, the
testing process, which is conducted using empirical data, demonstrates the
effective performance of the trained deep hedging agent in a realistic trading
environment.
