Sparse index clones via the sorted ℓ1-Norm

Index tracking and hedge fund replication aim at cloning the return time series properties of a given benchmark, by either using only a subset of its original constituents or by a set of risk factors. In this paper, we propose a model that relies on the Sorted Penalized Estimator, called SLOPE, for...

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Bibliographic Details
Published inQuantitative finance Vol. 22; no. 2; pp. 349 - 366
Main Authors Kremer, Philipp J., Brzyski, Damian, Bogdan, Małgorzata, Paterlini, Sandra
Format Journal Article
LanguageEnglish
Published Routledge 2022
Subjects
Economics and Business
Ekonomi och näringsliv
Hedge fund clones
Index tracking
Regularization
Samhällsvetenskap
SLOPE
Social Sciences
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Summary:Index tracking and hedge fund replication aim at cloning the return time series properties of a given benchmark, by either using only a subset of its original constituents or by a set of risk factors. In this paper, we propose a model that relies on the Sorted Penalized Estimator, called SLOPE, for index tracking and hedge fund replication. We show that SLOPE is capable of not only providing sparsity, but also to form groups among assets depending on their partial correlation with the index or the hedge fund return times series. The grouping structure can then be exploited to create individual investment strategies that allow building portfolios with a smaller number of active positions, but still comparable tracking properties. Considering equity index data and hedge fund returns, we discuss the real-world properties of SLOPE based approaches with respect to state-of-the art approaches.
ISSN:1469-7688
1469-7696
DOI:10.1080/14697688.2021.1962539