Paper: arXiv 2310.18755

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper integrates stochastic calculus (Heston model, DDPG) with extensive empirical validation (stylized facts metrics, transaction cost testing), demonstrating high math density and rigorous, backtest-ready methodology.

Research Flowchart

  flowchart TD
  A["Research Goal: Improve Deep Hedging Effectiveness"]
  B["Develop Agent-Based Model<br>Chiarella-Heston Model"]
  C["Calibrate & Validate Model<br>vs. Baselines (GBM, Extended Chiarella, Heston)"]
  D["Generate Training Dataset<br>Under Various Transaction Costs"]
  E["Train Deep Hedging Agent<br>using DDPG Algorithm"]
  F["Test Agent on Empirical Data<br>Measure Performance & Risk"]
  G["Outcome: Outperforms Baselines<br>Effective in Realistic Trading"]

  A --> B
  B --> C
  C --> D
  D --> E
  E --> F
  F --> G

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0 of 115 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.