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Optimal asset allocation for strategic investors

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  • Laborda, Ricardo
  • Olmo, Jose

Abstract

This paper studies optimal asset allocation for investors over multiple investment horizons. Rather than first model the various features of the conditional return distribution and subsequently characterize the portfolio choice, we focus directly on the dependence of the portfolio weights on the predictor variables through a linear parametric portfolio policy rule. This characterization allows us to apply GMM estimation and testing methods to sample analogues of the multiperiod Euler equations that characterize our optimal portfolio choice. Our model accommodates an arbitrarily large number of assets in the portfolio and state variables in the information set. The empirical results for a portfolio of stocks, bonds and cash provide ample support to the linear specification of the portfolio weights and reveal significant differences between myopic (one-period) and strategic (long-term) optimal portfolio allocations.

Suggested Citation

  • Laborda, Ricardo & Olmo, Jose, 2017. "Optimal asset allocation for strategic investors," International Journal of Forecasting, Elsevier, vol. 33(4), pages 970-987.
    • Handle: RePEc:eee:intfor:v:33:y:2017:i:4:p:970-987
    DOI: 10.1016/j.ijforecast.2017.05.003
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    1. Reza Bradrania & Davood Pirayesh Neghab, 2022. "State-dependent Asset Allocation Using Neural Networks," Papers 2211.00871, arXiv.org.
    2. Bradrania, Reza & Pirayesh Neghab, Davood, 2021. "State-dependent asset allocation using neural networks," MPRA Paper 115254, University Library of Munich, Germany.

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