Mean Reverting Portfolios via Penalized OU-Likelihood Estimation

We study an optimization-based approach to construct a mean-reverting portfolio of assets. Our objectives are threefold: (1) design a portfolio that is well-represented by an Ornstein-Uhlenbeck process with parameters estimated by maximum likelihood, (2) select portfolios with desirable characteristics of high mean reversion, and (3) select a parsimonious portfolio, i.e. find a small subset of a larger universe of assets that can be used for long and short positions. We present the full problem formulation, a specialized algorithm that exploits partial minimization, and numerical examples using both simulated and empirical price data.

Keywords: Mean Reversion, Maximum Likelihood Estimation, Ornstein-Uhlenbeck Process

JEL Classification: C58, C61, C63

Suggested Citation: Suggested Citation

Zhang, Jize and Leung, Tim and Aravkin, Aleksandr, Mean Reverting Portfolios via Penalized OU-Likelihood Estimation (August 8, 2018). In Proceedings of IEEE Conference on Decision and Control (CDC) 2018, Available at SSRN: https://ssrn.com/abstract=3142474 or http://dx.doi.org/10.2139/ssrn.3142474