# Calibrating rough volatility models: a convolutional neural network approach

## TL;DR

This paper employs convolutional neural networks to accurately estimate the H"older exponent in the rBergomi model, enhancing calibration techniques for complex financial models.

## Contribution

It introduces a novel CNN-based method for calibrating rough volatility models, specifically estimating the H"older exponent from simulated paths.

## Key findings

- CNN effectively estimates the H"older exponent
- Improves calibration accuracy for the rBergomi model
- Demonstrates practical application in financial modeling

## Abstract

In this paper we use convolutional neural networks to find the H\"older exponent of simulated sample paths of the rBergomi model, a recently proposed stock price model used in mathematical finance. We contextualise this as a calibration problem, thereby providing a very practical and useful application.