TY - JOUR T1 - Generating Virtual Scenarios of Multivariate Financial Data for Quantitative Trading Applications JF - The Journal of Financial Data Science DO - 10.3905/jfds.2019.1.003 SP - jfds.2019.1.003 AU - Javier Franco-Pedroso AU - Joaquin Gonzalez-Rodriguez AU - Jorge Cubero AU - Maria Planas AU - Rafael Cobo AU - Fernando Pablos Y1 - 2019/03/28 UR - https://pm-research.com/content/early/2019/03/28/jfds.2019.1.003.abstract N2 - 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. ER -