Malgorzata Bogdan
Professor
Sparse index clones via the sorted ℓ1-Norm
Författare
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