Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155740
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dc.contributor.authorPun, Chi Sengen_US
dc.date.accessioned2022-03-16T01:22:03Z-
dc.date.available2022-03-16T01:22:03Z-
dc.date.issued2021-
dc.identifier.citationPun, C. S. (2021). A sparse learning approach to relative-volatility-managed portfolio selection. SIAM Journal On Financial Mathematics, 12(1), 410-445. https://dx.doi.org/10.1137/19M1291674en_US
dc.identifier.issn1945-497Xen_US
dc.identifier.urihttps://hdl.handle.net/10356/155740-
dc.description.abstractThis paper proposes a self-calibrated sparse learning approach for estimating a sparse target vector, which is a product of a precision matrix and a vector, and investigates its application to finance to provide an innovative construction of a relative-volatility-managed portfolio. The proposed iterative algorithm, called DECODE, jointly estimates a performance measure of the market and the effective parameter vector in the optimal portfolio solution, where the relative-volatility timing is introduced into the risk exposure of an efficient portfolio via the control of its sparsity. The portfolio’s risk exposure level, which is linked to its sparsity in the proposed framework, is automatically tuned with the latest market condition without using cross validation. The algorithm is efficient as it costs only a few computations of quadratic programming. We prove that the iterative algorithm converges and show the oracle inequalities of the DECODE, which provide sufficient conditions for a consistent estimate of an optimal portfolio. The algorithm can also handle the curse of dimensionality in that the number of training samples is less than the number of assets. Our empirical studies of over-12-year backtest illustrate the relative-volatility timing feature of the DECODE and the superior out-of-sample performance of the DECODE portfolio, which beats the equally weighted portfolio and improves over the shrinkage portfolio.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relationM4082115en_US
dc.relationMOE2017-T2-1-044en_US
dc.relation.ispartofSIAM Journal on Financial Mathematicsen_US
dc.rights© 2021 Society for Industrial and Applied Mathematics. All rights reserved. This paper was published in SIAM Journal on Financial Mathematics and is made available with permission of Society for Industrial and Applied Mathematics.en_US
dc.subjectScience::Mathematicsen_US
dc.titleA sparse learning approach to relative-volatility-managed portfolio selectionen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.identifier.doi10.1137/19M1291674-
dc.description.versionSubmitted/Accepted versionen_US
dc.identifier.scopus2-s2.0-85104468660-
dc.identifier.issue1en_US
dc.identifier.volume12en_US
dc.identifier.spage410en_US
dc.identifier.epage445en_US
dc.subject.keywordsDirect Estimationen_US
dc.subject.keywordsIterative Algorithmen_US
dc.description.acknowledgementThis work was funded by the Data Science and Artificial Intelligence Research Centre at NanyangTechnological University, grant M4082115, and the Ministry of Education (Singapore), AcRF Tier 2 grant MOE2017-T2-1-044.en_US
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