Multi-Period Portfolio Optimization using Model Predictive Control with Mean-Variance and Risk Parity Frameworks
Portfolio Management
2021-03-22 v1
Abstract
We employ model predictive control for a multi-period portfolio optimization problem. In addition to the mean-variance objective, we construct a portfolio whose allocation is given by model predictive control with a risk-parity objective, and provide a successive convex program algorithm that provides 30 times faster and robust solutions in the experiments. Computational results on the multi-asset universe show that multi-period models perform better than their single period counterparts in out-of-sample period, 2006-2020. The out-of-sample risk-adjusted performance of both mean-variance and risk-parity formulations beat the fix-mix benchmark, and achieve Sharpe ratio of 0.64 and 0.97, respectively.
Keywords
Cite
@article{arxiv.2103.10813,
title = {Multi-Period Portfolio Optimization using Model Predictive Control with Mean-Variance and Risk Parity Frameworks},
author = {Xiaoyue Li and A. Sinem Uysal and John M. Mulvey},
journal= {arXiv preprint arXiv:2103.10813},
year = {2021}
}