Financial Engineering Workshop - Milena Vuletić (University of Oxford)

Presentation title: VolGAN: a generative model for arbitrage-free implied volatility surfaces

Abstract: We introduce VolGAN, a generative model for arbitrage-free implied volatility surfaces. The model is trained on time series of implied volatility surfaces and underlying prices and is capable of generating realistic scenarios for joint dynamics of the implied volatility surface and the underlying asset. We illustrate the performance of the model by training it on SPX implied volatility time series and show that it is able to learn the covariance structure of the co-movements in implied volatilities and generate realistic dynamics for the (VIX) volatility index. In particular, the generative model is capable of simulating scenarios with non-Gaussian distributions of increments for state variables as well as time-varying correlations.

Bio: Milena Vuletić is a DPhil candidate at the University of Oxford, specialising in computational finance within the Centre for Doctoral Training in Mathematics of Random Systems. She is supervised by Professors Rama Cont and Mihai Cucuringu, with her research focusing on mathematical and data-driven modeling of multi-asset markets. Her work incorporates advanced machine learning techniques, such as generative adversarial networks, to tackle challenges in quantitative finance, including volatility modelling and risk management. In November 2024, she won the “Rising Star in Quantitative Finance” award by Risk.net for her contribution to the field, notably VolGAN, a generative model for arbitrage-free implied volatility surfaces.

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