Calibrating rough volatility models: a convolutional neural network approach
Computational Finance
2019-07-30 v3
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.
Cite
@article{arxiv.1812.05315,
title = {Calibrating rough volatility models: a convolutional neural network approach},
author = {Henry Stone},
journal= {arXiv preprint arXiv:1812.05315},
year = {2019}
}