Technical note: a robust perspective on transaction costs in portfolio optimization
Journal
Operations Research
Subject
Management Science and Operations
Publishing details
Authors / Editors
Olivares-Nadal A V;DeMiguel V
Biographies
Publication Year
2018
Abstract
We prove that the portfolio problem with transaction costs is equivalent to three different problems designed to alleviate the impact of estimation error: a robust portfolio optimization problem, a regularized regression problem, and a Bayesian portfolio problem. Motivated by these results, we propose a data-driven approach to portfolio optimization that tackles transaction costs and estimation error simultaneously by treating the transaction costs as a regularization term to be calibrated. Our empirical results demonstrate that the data-driven portfolios perform favorably because they strike an optimal trade-o between rebalancing the portfolio to capture the information in recent historical return data, and avoiding the large transaction costs and impact of estimation error associated with excessive trading.
Keywords
Transaction costs; Estimation error; Robust optimization; Robust regression; Bayesian portfolios; Data-driven portfolios.
Available on ECCH
No