Paper: arXiv 2411.19317

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.

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: 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.

Research Flowchart

  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

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4 of 73 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.