RT Journal Article
SR Electronic
T1 Generating Virtual Scenarios of Multivariate Financial Data for Quantitative Trading Applications
JF The Journal of Financial Data Science
FD Institutional Investor Journals
SP 55
OP 77
DO 10.3905/jfds.2019.1.003
VO 1
IS 2
A1 Javier Franco-Pedroso
A1 Joaquin Gonzalez-Rodriguez
A1 Jorge Cubero
A1 Maria Planas
A1 Rafael Cobo
A1 Fernando Pablos
YR 2019
UL https://pm-research.com/content/1/2/55.abstract
AB 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.TOPICS: Simulations, performance measurement