Generating Virtual Scenarios of Multivariate Financial Data for Quantitative Trading Applications

Abstract

In this article, the authors present a novel approach to the generation of virtual scenarios of multivariate financial data of arbitrary length and composition of assets. With this approach, decades of realistic time-synchronized data can be simulated for a large number of assets, producing diverse scenarios to test and improve quantitative investment strategies. The authors’ approach is based on the analysis and synthesis of the time-dependent individual and joint characteristics of real financial time series, using stochastic sequences of market trends to draw multivariate returns from time-dependent probability functions that preserve both distributional properties of asset returns and time-dependent correlation among time series. Moreover, new time-synchronized assets can be arbitrarily generated through a principal component analysis–based procedure to obtain any number of assets in the final virtual scenario. The validation of such a simulation is tested with an extensive set of measurements and shows a significant degree of agreement with the reference performance of real financial series—better than that obtained with other classical and state-of-the-art approaches.