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

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 pa...

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Detalles Bibliográficos
Autores principales: Reus, Lorenzo, Prado, Rodolfo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8249440/
https://www.ncbi.nlm.nih.gov/pubmed/34230769
http://dx.doi.org/10.1007/s10614-021-10133-6
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author Reus, Lorenzo
Prado, Rodolfo
author_facet Reus, Lorenzo
Prado, Rodolfo
author_sort Reus, Lorenzo
collection PubMed
description 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.
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spelling pubmed-82494402021-07-02 Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method Reus, Lorenzo Prado, Rodolfo Comput Econ Article 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. Springer US 2021-07-02 2022 /pmc/articles/PMC8249440/ /pubmed/34230769 http://dx.doi.org/10.1007/s10614-021-10133-6 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Reus, Lorenzo
Prado, Rodolfo
Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method
title Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method
title_full Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method
title_fullStr Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method
title_full_unstemmed Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method
title_short Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method
title_sort need to meet investment goals? track synthetic indexes with the sddp method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8249440/
https://www.ncbi.nlm.nih.gov/pubmed/34230769
http://dx.doi.org/10.1007/s10614-021-10133-6
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