Sparse index clones via the sorted ℓ1-Norm
(2022) In Quantitative Finance 22(2). p.349-366- Abstract
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... (More)
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.
(Less)
- author
- Kremer, Philipp J. ; Brzyski, Damian ; Bogdan, Małgorzata LU and Paterlini, Sandra
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Hedge fund clones, Index tracking, Regularization, SLOPE
- in
- Quantitative Finance
- volume
- 22
- issue
- 2
- pages
- 349 - 366
- publisher
- Taylor & Francis
- external identifiers
-
- pmid:35465255
- scopus:85115151707
- ISSN
- 1469-7688
- DOI
- 10.1080/14697688.2021.1962539
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2021 Informa UK Limited, trading as Taylor & Francis Group.
- id
- 57b1feb3-1d52-4396-9a23-b9654920b9b8
- date added to LUP
- 2021-10-12 15:55:44
- date last changed
- 2024-06-15 18:06:11
@article{57b1feb3-1d52-4396-9a23-b9654920b9b8,
abstract = {{<p>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.</p>}},
author = {{Kremer, Philipp J. and Brzyski, Damian and Bogdan, Małgorzata and Paterlini, Sandra}},
issn = {{1469-7688}},
keywords = {{Hedge fund clones; Index tracking; Regularization; SLOPE}},
language = {{eng}},
number = {{2}},
pages = {{349--366}},
publisher = {{Taylor & Francis}},
series = {{Quantitative Finance}},
title = {{Sparse index clones via the sorted ℓ<sub>1</sub>-Norm}},
url = {{http://dx.doi.org/10.1080/14697688.2021.1962539}},
doi = {{10.1080/14697688.2021.1962539}},
volume = {{22}},
year = {{2022}},
}