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Malgorzata Bogdan

Professor

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Sparse index clones via the sorted ℓ1-Norm

Författare

  • Philipp J. Kremer
  • Damian Brzyski
  • Małgorzata Bogdan
  • Sandra Paterlini

Summary, in English

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 (Formula presented.) 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.

Avdelning/ar

  • Statistiska institutionen

Publiceringsår

2022

Språk

Engelska

Sidor

349-366

Publikation/Tidskrift/Serie

Quantitative Finance

Volym

22

Issue

2

Dokumenttyp

Artikel i tidskrift

Förlag

Taylor & Francis

Ämne

  • Economics and Business

Nyckelord

  • Hedge fund clones
  • Index tracking
  • Regularization
  • SLOPE

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1469-7688