A Data-Driven Market Simulator for Small Data Environments

27 Pages Posted: 14 Jul 2020

See all articles by Hans Buehler

Hans Buehler

JP Morgan

Blanka Horvath

ETH Zürich - Department of Mathematics

Terry Lyons

affiliation not provided to SSRN

Imanol Perez Arribas

University of Oxford - Mathematical Institute

Ben Wood

JP Morgan Chase

Date Written: June 21, 2020

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 paths 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.

Suggested Citation

Buehler, Hans and Horvath, Blanka and Lyons, Terry and Perez Arribas, Imanol and Wood, Ben, A Data-Driven Market Simulator for Small Data Environments (June 21, 2020). Available at SSRN: https://ssrn.com/abstract=3632431 or http://dx.doi.org/10.2139/ssrn.3632431

Hans Buehler

JP Morgan ( email )

London
United Kingdom

Blanka Horvath

ETH Zürich - Department of Mathematics ( email )

R¨amistrasse 101
Raemistr. 101
Z¨urich, 8092
Switzerland

Terry Lyons

affiliation not provided to SSRN

Imanol Perez Arribas (Contact Author)

University of Oxford - Mathematical Institute ( email )

Andrew Wiles Building
Radcliffe Observatory Quarter (550)
Oxford, OX2 6GG
United Kingdom

Ben Wood

JP Morgan Chase ( email )

London
United Kingdom

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