Title: Mean Reverting Portfolios via Penalized OU-Likelihood Estimation,
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.