On deep calibration of (rough) stochastic volatility models

TL;DR

This paper introduces a two-step deep learning approach for calibrating rough stochastic volatility models by first learning an accurate pricing map with neural networks, then applying traditional calibration methods for efficient and practical model fitting.

Contribution

It proposes a novel two-step calibration method that uses deep learning solely for pricing map approximation, enabling efficient calibration of complex rough volatility models.

Findings

Neural network-based pricing maps achieve sufficient accuracy for practical calibration.

The method is successfully applied to the rough Bergomi model, overcoming computational challenges.

Different sampling and training strategies are compared for optimal performance.

Abstract

Techniques from deep learning play a more and more important role for the important task of calibration of financial models. The pioneering paper by Hernandez [Risk, 2017] was a catalyst for resurfacing interest in research in this area. In this paper we advocate an alternative (two-step) approach using deep learning techniques solely to learn the pricing map -- from model parameters to prices or implied volatilities -- rather than directly the calibrated model parameters as a function of observed market data. Having a fast and accurate neural-network-based approximating pricing map (first step), we can then (second step) use traditional model calibration algorithms. In this work we showcase a direct comparison of different potential approaches to the learning stage and present algorithms that provide a suffcient accuracy for practical use. We provide a first neural network-based calibration method for rough volatility models for which calibration can be done on the y. We demonstrate the method via a hands-on calibration engine on the rough Bergomi model, for which classical calibration techniques are diffcult to apply due to the high cost of all known numerical pricing methods. Furthermore, we display and compare different types of sampling and training methods and elaborate on their advantages under different objectives. As a further application we use the fast pricing method for a Bayesian analysis of the calibrated model.