Power mapping and noise reduction for financial correlations
(2005) Conference on Applications of Random Matrices to Economy and Other Complex Systems 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:
https://lup.lub.lu.se/record/616485
- author
- Andersson, Per-Johan ; Öberg, Andreas and Guhr, Thomas LU
- organization
- publishing date
- 2005
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Stock portfolios, Shrinkage method, Financial correlation matrices, Financial time series
- host publication
- Acta Physica Polonica, Series B
- volume
- 36
- issue
- 9
- pages
- 2611 - 2619
- publisher
- Jagiellonian University, Cracow, Poland
- conference name
- Conference on Applications of Random Matrices to Economy and Other Complex Systems
- conference location
- Cracow, Poland
- conference dates
- 2005-05-25 - 2005-05-28
- external identifiers
-
- 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
- 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
- 2016-04-01 16:05:48
- date last changed
- 2022-01-28 17:17:15
@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}}, keywords = {{Stock portfolios; Shrinkage method; Financial correlation matrices; Financial time series}}, language = {{eng}}, number = {{9}}, pages = {{2611--2619}}, publisher = {{Jagiellonian University, Cracow, Poland}}, 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}}, }