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

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  • Xiaoyue Li
  • A. Sinem Uysal
  • John M. Mulvey

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.

Suggested Citation

  • Xiaoyue Li & A. Sinem Uysal & John M. Mulvey, 2021. "Multi-Period Portfolio Optimization using Model Predictive Control with Mean-Variance and Risk Parity Frameworks," Papers 2103.10813, arXiv.org.
    • Handle: RePEc:arx:papers:2103.10813
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    References listed on IDEAS

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    Cited by:

    1. da Costa, B. Freitas Paulo & Pesenti, Silvana M. & Targino, Rodrigo S., 2023. "Risk budgeting portfolios from simulations," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1040-1056.
    2. Bernardo Freitas Paulo da Costa & Silvana M. Pesenti & Rodrigo S. Targino, 2023. "Risk Budgeting Portfolios from Simulations," Papers 2302.01196, arXiv.org.
    3. Zhang, Cheng & Gong, Xiaomin & Zhang, Jingshu & Chen, Zhiwei, 2023. "Dynamic portfolio allocation for financial markets: A perspective of competitive-cum-compensatory strategy," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).

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