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Power mapping and noise reduction for financial correlations

Andersson, Per-Johan; Öberg, Andreas and Guhr, Thomas LU (2005) Conference on Applications of Random Matrices to Economy and Other Complex Systems In Acta Physica Polonica, Series B 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 rle of constraints by excluding short selling in the optimization. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Stock portfolios, Shrinkage method, Financial correlation matrices, Financial time series
in
Acta Physica Polonica, Series B
volume
36
issue
9
pages
2611 - 2619
publisher
Jagellonian University, Cracow, Poland
conference name
Conference on Applications of Random Matrices to Economy and Other Complex Systems
external identifiers
  • scopus:33644966129
ISSN
0587-4254
language
English
LU publication?
yes
id
65ad7bc6-22a6-4ad7-9b00-7878cb393960 (old id 616485)
alternative location
http://th-www.if.uj.edu.pl/acta/vol36/pdf/v36p2611.pdf
date added to LUP
2007-11-25 09:16:43
date last changed
2017-01-01 06:55:32
@inproceedings{65ad7bc6-22a6-4ad7-9b00-7878cb393960,
  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 rle of constraints by excluding short selling in the optimization.},
  author       = {Andersson, Per-Johan and Öberg, Andreas and Guhr, Thomas},
  booktitle    = {Acta Physica Polonica, Series B},
  issn         = {0587-4254},
  keyword      = {Stock portfolios,Shrinkage method,Financial correlation matrices,Financial time series},
  language     = {eng},
  number       = {9},
  pages        = {2611--2619},
  publisher    = {Jagellonian University, Cracow, Poland},
  title        = {Power mapping and noise reduction for financial correlations},
  volume       = {36},
  year         = {2005},
}