Deep learning interpretability for rough volatility

ArXiv ID: 2411.19317 “View on arXiv”

Authors: Unknown

Abstract

Deep learning methods have become a widespread toolbox for pricing and calibration of financial models. While they often provide new directions and research results, their `black box’ nature also results in a lack of interpretability. We provide a detailed interpretability analysis of these methods in the context of rough volatility - a new class of volatility models for Equity and FX markets. Our work sheds light on the neural network learned inverse map between the rough volatility model parameters, seen as mathematical model inputs and network outputs, and the resulting implied volatility across strikes and maturities, seen as mathematical model outputs and network inputs. This contributes to building a solid framework for a safer use of neural networks in this context and in quantitative finance more generally.

Keywords: Deep Learning, Rough Volatility, Neural Network Calibration, Implied Volatility, Model Interpretability, Equities and FX

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats
  • Why: The paper employs advanced mathematics including stochastic calculus for rough Heston models, complex neural network architectures, and sophisticated interpretability methods like SHAP and LIME. However, it focuses on theoretical interpretability analysis using synthetic data without reporting real-market backtests or performance metrics.
  flowchart TD
    A["Research Goal: Interpret the deep learning inverse map<br>in rough volatility model calibration"] --> B["Data Input:<br>Model Parameters & Implied Volatility Surfaces"]
    B --> C["Method: Neural Network<br>Calibration Architecture"]
    C --> D["Computational Process:<br>Forward Pass (IV Surface -> Parameters)<br>Backward Pass (Sensitivity Analysis)"]
    D --> E["Key Finding 1: Learned<br>Parameter Sensitivity Patterns"]
    D --> F["Key Finding 2: Non-linear<br>Relationships Exposed"]
    E --> G["Outcome: Enhanced Model<br>Interpretability Framework"]
    F --> G