A Model-Based Synthetic Stock Price Time Series Generation Framework
Computational Engineering, Finance, and Science
2023-11-07 v1 Databases
Abstract
The Ornstein-Uhlenbeck (OU) process, a mean-reverting stochastic process, has been widely applied as a time series model in various domains. This paper describes the design and implementation of a model-based synthetic time series model based on a multivariate OU process and the Arbitrage Pricing Theory (APT) for generating synthetic pricing data for a complex market of interacting stocks. The objective is to create a group of synthetic stock price time series that reflects the correlation between individual stocks and clusters of stocks in how a real market behaves. We demonstrate the method using the Standard and Poor's (S&P) 500 universe of stocks as an example.
Cite
@article{arxiv.2311.02209,
title = {A Model-Based Synthetic Stock Price Time Series Generation Framework},
author = {Haibei Zhu and Svitlana Vyetrenko and Tucker Balch},
journal= {arXiv preprint arXiv:2311.02209},
year = {2023}
}