A Data-Driven Market Simulator for Small Data Environments

Hans Bühler (), Blanka Horvath (), Terry Lyons (), Imanol Perez Arribas () and Ben Wood ()
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Hans Bühler: XTX Markets
Blanka Horvath: University of Oxford
Terry Lyons: University of Oxford and The Alan Turing Institute, Mathematical Institute
Imanol Perez Arribas: University of Oxford and The Alan Turing Institute, Mathematical Institute
Ben Wood: J.P. Morgan

A chapter in Stochastic Analysis and Applications 2025, 2026, pp 273-310 from Springer

Abstract: Abstract 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 dynamics. Though in this sense generative market simulation is model-free, the concrete modelling choices are nevertheless decisive for the features of the simulated paths. We give a brief overview of currently used generative modelling approaches and performance evaluation metrics for financial time series, and address some of the challenges to achieve good results in the latter. We also contrast some classical approaches of market simulation with simulation based on generative modelling and highlight some advantages and pitfalls of the new approach. While most generative models tend to rely on large amounts of training data, we present here a generative model that works reliably even in environments where the amount of available training data is notoriously small. Furthermore, we show how a rough path perspective combined with a parsimonious variational autoencoder framework provides a powerful way for encoding and evaluating financial time series in such environments where available training data is scarce. Finally, we also propose a suitable performance evaluation metric for financial time series and discuss some connections of our Market Generator to deep hedging.

Keywords: 60G20; 60G22; 60L10; 60L20; 60L90; 91G60; 91G70; 91G80; 91G99 (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-03914-9_10

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