We survey the active interface of statistical learning methods and quantitative finance
models. Our focus is on the use of statistical surrogates, also known as functional …

Neural network based data-driven market simulation unveils a new and flexible way of
modelling financial time series without imposing assumptions on the underlying stochastic …

The universal approximation property of various machine learning models is currently only
understood on a case-by-case basis, limiting the rapid development of new theoretically …

We consider the joint SPX-VIX calibration within a general class of Gaussian polynomial
volatility models in which the volatility of the SPX is assumed to be a polynomial function of a …

We conduct an empirical analysis of rough and classical stochastic volatility models to the
SPX and VIX options markets. Our analysis focusses primarily on calibration quality and is …

The quadratic rough Heston model provides a natural way to encode Zumbach effect in the
rough volatility paradigm. We apply multi-factor approximation and use deep learning …

Since we will never really know why the prices of financial assets move, we should at least
make a faithful model of how they move. This was the motivation of Bachelier in 1900, when …

We investigate the performance of the Deep Hedging framework under training paths
beyond the (finite dimensional) Markovian setup. In particular, we analyse the hedging …

Optimization over trained machine learning models has applications including: verification,
minimizing neural acquisition functions, and integrating a trained surrogate into a larger …

Recent progress in the field of artificial intelligence, machine learning and also in computer
industry resulted in the ongoing boom of using these techniques as applied to solving …