# Generating virtual scenarios of multivariate financial data for quantitative trading applications

arXiv: https://arxiv.org/abs/1802.01861 · HTML: https://mlsanity.com/paper/1802.01861/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Scenario generation for market risk models using generative neural networks](https://mlsanity.com/paper/2109.10072/) (2021)

## Abstract (arXiv)

In this paper, we 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. Our 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 preserving both distributional properties of asset returns and time-dependent correlation among time series. Moreover, new time-synchronized assets can be arbitrarily generated through a PCA-based procedure to obtain any number of assets in the final virtual scenario. For the validation of such simulated data, they are tested with an extensive set of measurements showing 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.