Multi-Period Portfolio Optimization using Model Predictive Control with Mean-Variance and Risk Parity Frameworks

TL;DR

This paper introduces a model predictive control approach for multi-period portfolio optimization, integrating mean-variance and risk-parity frameworks, and demonstrates improved out-of-sample performance and computational efficiency over traditional methods.

Contribution

It develops a novel successive convex programming algorithm for multi-period portfolio optimization with mean-variance and risk-parity objectives, achieving faster solutions and better out-of-sample results.

Findings

Multi-period models outperform single-period models in out-of-sample tests.

Both mean-variance and risk-parity approaches surpass the benchmark in risk-adjusted returns.

The proposed algorithm is 30 times faster and more robust than existing methods.

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.