On Deep Calibration of (rough) Stochastic Volatility Models

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, A., 2017, Model calibration with neural networks, Risk] 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. 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 sufficient accuracy for practical use. In particular provide the first and simplest neural network-based calibration methods for rough volatility models with a constant initial volatility for which calibration can be done on the fly. We demonstrate the method via a hands-on calibration engine on the rough Bergomi model, for which classical calibration techniques are difficult 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.