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Power mapping and noise reduction for financial correlations

Andersson, P-J; Oberg, A and Guhr, Thomas LU (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)
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author
organization
publishing date
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
Jagellonian University, Cracow, Poland
external identifiers
  • wos:000232226500002
  • scopus:33644966129
ISSN
0587-4254
language
English
LU publication?
yes
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
2007-08-21 11:48:48
date last changed
2017-01-01 06:51:03
@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    = {Jagellonian 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},
  volume       = {36},
  year         = {2005},
}