On the Limitations of Neural Networks for Option Pricing: Analysis of Volatility Regime Sensitivity

Tarun Raheja, Nilay Pochhi

Track: long paper (up to 4 pages)

Keywords: Option Pricing, Neural Networks, Volatility Regimes, Distribution Shift, Financial Machine Learning, Model Robustness, Black-Scholes Approximation, Deep Learning Limitations

TL;DR: Neural networks trained on low-volatility option data (σ=0.2) systematically fail on high-volatility scenarios (σ=0.3), revealing fundamental limitations in their ability to adapt to market regime changes.

Abstract:

Recent work demonstrates neural networks' theoretical ability to approximate option pricing functions, but empirical evidence regarding robustness to market regime shifts remains limited. Motivated by practical scenarios where the classical deterministic Black-Scholes equation becomes computationally challenging in high-dimensional settings or under complex market conditions, we examine neural network performance during volatility regime transitions. Models trained on low-volatility regimes ($\sigma=0.2$) show significant errors under higher volatility ($\sigma=0.3$). We provide detailed theoretical and empirical analyses indicating that these errors reflect fundamental representational limits of current architectures rather than optimization issues.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 38