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arXiv · 1803.06460

Mean Reverting Portfolios via Penalized OU-Likelihood Estimation

Jize Zhang·Tim Leung·Aleksandr Y. Aravkin
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Abstract

We study an optimization-based approach to con- struct 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 low variance, 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.

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Jize Zhang, Tim Leung, Aleksandr Y. Aravkin. 2018-03-17. Mean Reverting Portfolios via Penalized OU-Likelihood Estimation. https://arxiv.org/abs/1803.06460

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