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Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method

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  • Lorenzo Reus

    (Universidad Adolfo Ibañez)

  • Rodolfo Prado

    (Universidad Adolfo Ibañez)

Abstract

This work presents a novel application of the Stochastic Dual Dynamic Problem (SDDP) to large-scale asset allocation. We construct a model that delivers allocation policies based on how the portfolio performs with respect to user-defined (synthetic) indexes, and implement it in a SDDP open-source package. Based on US economic cycles and ETF data, we generate Markovian regime-dependent returns to solve an instance of multiple assets and 28 time periods. Results show our solution outperforms its benchmark, in both profitability and tracking error.

Suggested Citation

  • Lorenzo Reus & Rodolfo Prado, 2022. "Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 47-69, June.
    • Handle: RePEc:kap:compec:v:60:y:2022:i:1:d:10.1007_s10614-021-10133-6
    DOI: 10.1007/s10614-021-10133-6
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    References listed on IDEAS

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