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

Lorenzo Reus, Rodolfo Prado

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

Resumen

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.

Idioma originalInglés
Páginas (desde-hasta)47-69
Número de páginas23
PublicaciónComputational Economics
Volumen60
N.º1
DOI
EstadoPublicada - jun. 2022
Publicado de forma externa

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