A Model-Based Synthetic Stock Price Time Series Generation Framework

TL;DR

This paper presents a novel framework for generating synthetic stock price time series using a multivariate Ornstein-Uhlenbeck process combined with Arbitrage Pricing Theory to mimic real market behaviors and stock correlations.

Contribution

It introduces a model-based approach that integrates OU processes with APT to produce realistic synthetic market data reflecting stock interactions.

Findings

Successfully generates synthetic stock data resembling real market correlations.

Demonstrates the method on S&P 500 stocks, capturing key market dynamics.

Provides a flexible framework for synthetic data generation in financial modeling.

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.