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An Analysis of Neural Network Architectures for Deep Quadratic Hedging

2024
  • 1
    Citations
  • 319
    Usage
  • 0
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    1
    • Citation Indexes
      1
  • Usage
    319
    • Abstract Views
      252
    • Downloads
      67
  • Ratings
    • Download Rank
      694,033

Paper Description

This paper considers the deep stochastic optimal control methodology and examines the model uncertainty resulting from the choice of neural network architecture for quadratic hedging of a European Call option with transaction cost, under a discrete trading schedule. Researchers and quantitative finance practitioners have introduced various bespoke neural network architectures for the problem of deep hedging; however, we notice that methodology benchmarking in the literature has been limited to high-level performance analysis. Recognizing the intimate relationship between the deep stochastic optimal control and certain solution methods for trajectory optimization problems, we examine the interaction between model training and the choice of neural network architecture. We show that parameterizing the hedge ratio policies at each time step by an independent neural network is more consistent with the dynamics of the gradients in the ADAM optimization and results in better training and higher performance.

Bibliographic Details

Ahmad Aghapour; Hamid R. Arian; Ali Fathi

Elsevier BV

Derivative Hedging; Stochastic Optimal Control; Trajectory Optimization; Deep Learning

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