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In this article, we explore generative models in order to build a market generator. The underlying idea is to simulate artificial multi-dimensional financial time series, whose statistical properties are the same as those observed in the financial markets. In particular, these synthetic data must preserve the first four statistical moments (mean, standard deviation, skewness and kurtosis), the stochastic dependence between the different dimensions (copula structure) and across time (autocorrelation function). The first part of the article reviews the more relevant generative models, which are restricted Boltzmann machines, generative adversarial networks, and convolutional Wasserstein models. The second part of the article is dedicated to financial applications by considering the simulation of multi-dimensional times series and estimating the probability distribution of backtest statistics. The final objective is to develop a framework for improving the risk management of quantitative investment strategies.
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