Dear all, I invite you to the seminar "Deep learning volatility", that will be given by Dr. Blanka Horvath (King's College - Londra), next Tuesday at 11.30, in room 2BC30 of the Mathematics Department in Padova.
The abstract follows:
* Abstract:
We present a consistent neural network based calibration method for a number of volatility models-including the rough volatility family-that performs the calibration task within a few milliseconds for the full implied volatility surface.
The aim of neural networks in this work is an off-line approximation of complex pricing functions, which are difficult to represent or time-consuming to evaluate by other means. We highlight how this perspective opens new horizons for quantitative modelling: The calibration bottleneck posed by a slow pricing of derivative contracts is lifted. This brings several model families (such as rough volatility models) within the scope of applicability in industry practice. As customary for machine learning, the form in which information from available data is extracted and stored is crucial for network performance. With this in mind we discuss how our approach addresses the usual challenges of machine learning solutions in a financial context (availability of training data, interpretability of results for regulators, control over generalisation errors). We present specific architectures for price approximation and calibration and optimize these with respect different objectives regarding accuracy, speed and robustness. We also find that including the intermediate step of learning pricing functions of (classical or rough) models before calibration significantly improves network performance compared to direct calibration to data. The talk is based on joint work with A. Muguruza an M. Tomas, available here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3322085
See you there (if interested!), Giorgia