Deep learning interpretability for rough volatility

TL;DR

This paper analyzes the interpretability of deep learning models used for rough volatility in finance, focusing on understanding the neural network's learned inverse mapping between model parameters and implied volatility.

Contribution

It provides a detailed interpretability analysis of neural networks in rough volatility models, enhancing understanding and safer application in quantitative finance.

Findings

Clarifies the neural network's inverse mapping in rough volatility models

Provides insights into the network's learned relationships between parameters and outputs

Contributes to safer and more transparent use of neural networks in finance

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.