Power mapping and noise reduction for financial correlations
(2005) In Acta Physica Polonica. Series B: Elementary Particle Physics, Nuclear Physics, Statistical Physics, Theory of Relativity, Field Theory 36(9). p.2611-2619- Abstract
- The spectral properties of financial correlation matrices can show features known from completely random matrices. A major reason is noise originating from the finite lengths of the financial time series used to compute the correlation matrix elements. In recent years, various methods have been proposed to reduce this noise, i.e. to clean the correlation matrices. This is of direct practical relevance for risk management in portfolio optimization. In this contribution, we discuss in detail the power mapping, a new shrinkage method. We show that the relevant parameter is, to a certain extent, self-determined. Due to the "chirality" and the normalization of the correlation matrix, the optimal shrinkage parameter is fixed. We apply the power... (More)
- The spectral properties of financial correlation matrices can show features known from completely random matrices. A major reason is noise originating from the finite lengths of the financial time series used to compute the correlation matrix elements. In recent years, various methods have been proposed to reduce this noise, i.e. to clean the correlation matrices. This is of direct practical relevance for risk management in portfolio optimization. In this contribution, we discuss in detail the power mapping, a new shrinkage method. We show that the relevant parameter is, to a certain extent, self-determined. Due to the "chirality" and the normalization of the correlation matrix, the optimal shrinkage parameter is fixed. We apply the power mapping and the well-known filtering method, to market data and compare them by optimizing stock portfolios. We address the role of constraints by excluding short selling in the optimization. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/223570
- author
- Andersson, P-J ; Oberg, A and Guhr, Thomas LU
- organization
- publishing date
- 2005
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Acta Physica Polonica. Series B: Elementary Particle Physics, Nuclear Physics, Statistical Physics, Theory of Relativity, Field Theory
- volume
- 36
- issue
- 9
- pages
- 2611 - 2619
- publisher
- Jagiellonian University, Cracow, Poland
- external identifiers
-
- wos:000232226500002
- scopus:33644966129
- ISSN
- 0587-4254
- language
- English
- LU publication?
- yes
- additional info
- The information about affiliations in this record was updated in December 2015. The record was previously connected to the following departments: Mathematical Physics (Faculty of Technology) (011040002)
- id
- 8f495f43-b0a9-4bfe-96c0-ab8f8bafd1c8 (old id 223570)
- alternative location
- http://th-www.if.uj.edu.pl/acta/vol36/pdf/v36p2611.pdf
- date added to LUP
- 2016-04-01 15:56:23
- date last changed
- 2022-01-28 08:11:27
@article{8f495f43-b0a9-4bfe-96c0-ab8f8bafd1c8,
abstract = {{The spectral properties of financial correlation matrices can show features known from completely random matrices. A major reason is noise originating from the finite lengths of the financial time series used to compute the correlation matrix elements. In recent years, various methods have been proposed to reduce this noise, i.e. to clean the correlation matrices. This is of direct practical relevance for risk management in portfolio optimization. In this contribution, we discuss in detail the power mapping, a new shrinkage method. We show that the relevant parameter is, to a certain extent, self-determined. Due to the "chirality" and the normalization of the correlation matrix, the optimal shrinkage parameter is fixed. We apply the power mapping and the well-known filtering method, to market data and compare them by optimizing stock portfolios. We address the role of constraints by excluding short selling in the optimization.}},
author = {{Andersson, P-J and Oberg, A and Guhr, Thomas}},
issn = {{0587-4254}},
language = {{eng}},
number = {{9}},
pages = {{2611--2619}},
publisher = {{Jagiellonian University, Cracow, Poland}},
series = {{Acta Physica Polonica. Series B: Elementary Particle Physics, Nuclear Physics, Statistical Physics, Theory of Relativity, Field Theory}},
title = {{Power mapping and noise reduction for financial correlations}},
url = {{http://th-www.if.uj.edu.pl/acta/vol36/pdf/v36p2611.pdf}},
volume = {{36}},
year = {{2005}},
}