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 multidimensional 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.
- Other research | Manager Intelligence and Market Trends
- T. Rowe Price | Better Virus Control May Help Europe to Outperform the U.S.
- NN Investment Partners | Multi Asset Monthly - Global Strategy
- Lyxor | Money Monitor: July 2020
- MFS Investment Management | The Case for (Eventual) Inflation
- WisdomTree | Daily Market Update - 11 August 2020
- Columbia Threadneedle | In Credit - Credit enjoying the summer lull
- Allianz Global Investors | How will private debt be changed by Covid-19?
- Amundi Asset Managers | Asset Class Return Forecasts
- Blue Bay Asset Management | High yield update