Paper: arXiv 2404.09462

Abstract

Derivative hedging and pricing are important and continuously studied topics in financial markets. Recently, deep hedging has been proposed as a promising approach that uses deep learning to approximate the optimal hedging strategy and can handle incomplete markets. However, deep hedging usually requires underlying asset simulations, and it is challenging to select the best model for such simulations. This study proposes a new approach using artificial market simulations for underlying asset simulations in deep hedging. Artificial market simulations can replicate the stylized facts of financial markets, and they seem to be a promising approach for deep hedging. We investigate the effectiveness of the proposed approach by comparing its results with those of the traditional approach, which uses mathematical finance models such as Brownian motion and Heston models for underlying asset simulations. The results show that the proposed approach can achieve almost the same level of performance as the traditional approach without mathematical finance models. Finally, we also reveal that the proposed approach has some limitations in terms of performance under certain conditions.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper involves advanced mathematical finance models (e.g., Heston, rBergomi) and deep learning, demonstrating high math complexity. It employs empirical methods through artificial market simulations and comparisons with traditional models, showing substantial implementation and testing, though without real-world backtests, resulting in high empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal: Evaluate<br>Artificial Market Simulations<br>for Deep Hedging"] --> B["Methodology: Comparative Experiment"]
  B --> C["Inputs: Option Pricing &<br>Underlying Asset Data"]
  C --> D["Computational Process 1: Deep Hedging<br>with Traditional Models Heston/BM"]
  C --> E["Computational Process 2: Deep Hedging<br>with Proposed Models Artificial Market Simulations"]
  D & E --> F["Key Findings & Outcomes"]
  F --> G["Performance: Proposed approach<br>achieves similar results to<br>traditional approach"]
  F --> H["Limitations: Performance drops<br>under specific market conditions"]

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1 of 49 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.