A new method to estimate the noise in financial correlation matrices
(2003) In Journal of Physics A: Mathematical and General 36(12). p.3009-3032- Abstract
- Companies belonging to the same industrial branch are subject to similar economical influences. Hence, the time series of their stocks can show similar trends implying a correlation. Financial correlation matrices measure the unsystematic correlations between time series of stocks. Such information is important for risk management. It has been found by Laloux et al that the correlation matrices are 'noise dressed', a major reason being the finiteness of the time series. We present a new and alternative method to estimate this noise. We introduce a power mapping of the elements in the correlation matrix which suppresses the noise and thereby effectively 'prolongs' the time series. Neither further data processing nor additional input is... (More)
- Companies belonging to the same industrial branch are subject to similar economical influences. Hence, the time series of their stocks can show similar trends implying a correlation. Financial correlation matrices measure the unsystematic correlations between time series of stocks. Such information is important for risk management. It has been found by Laloux et al that the correlation matrices are 'noise dressed', a major reason being the finiteness of the time series. We present a new and alternative method to estimate this noise. We introduce a power mapping of the elements in the correlation matrix which suppresses the noise and thereby effectively 'prolongs' the time series. Neither further data processing nor additional input is needed. To develop and test our method, we use a model suggested by Noh which can be viewed as a special case of a 'factor model' in economics. We perform numerical simulations for the time series and obtain correlation matrices. We support the numerics by a qualitative analytical discussion. With our approach, different correlation structures buried under this noise can be detected. Our method is general and can be applied to all systems in which time series are measured. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/312688
- author
- Guhr, Thomas LU and Kalber, Bernd
- organization
- publishing date
- 2003
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Physics A: Mathematical and General
- volume
- 36
- issue
- 12
- pages
- 3009 - 3032
- publisher
- IOP Publishing
- external identifiers
-
- wos:000182454900011
- scopus:0242266519
- ISSN
- 0305-4470
- DOI
- 10.1088/0305-4470/36/12/310
- 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
- 9e8327c7-2989-4078-9df5-060ac32079a6 (old id 312688)
- date added to LUP
- 2016-04-01 17:08:05
- date last changed
- 2022-03-15 05:21:26
@article{9e8327c7-2989-4078-9df5-060ac32079a6,
abstract = {{Companies belonging to the same industrial branch are subject to similar economical influences. Hence, the time series of their stocks can show similar trends implying a correlation. Financial correlation matrices measure the unsystematic correlations between time series of stocks. Such information is important for risk management. It has been found by Laloux et al that the correlation matrices are 'noise dressed', a major reason being the finiteness of the time series. We present a new and alternative method to estimate this noise. We introduce a power mapping of the elements in the correlation matrix which suppresses the noise and thereby effectively 'prolongs' the time series. Neither further data processing nor additional input is needed. To develop and test our method, we use a model suggested by Noh which can be viewed as a special case of a 'factor model' in economics. We perform numerical simulations for the time series and obtain correlation matrices. We support the numerics by a qualitative analytical discussion. With our approach, different correlation structures buried under this noise can be detected. Our method is general and can be applied to all systems in which time series are measured.}},
author = {{Guhr, Thomas and Kalber, Bernd}},
issn = {{0305-4470}},
language = {{eng}},
number = {{12}},
pages = {{3009--3032}},
publisher = {{IOP Publishing}},
series = {{Journal of Physics A: Mathematical and General}},
title = {{A new method to estimate the noise in financial correlation matrices}},
url = {{http://dx.doi.org/10.1088/0305-4470/36/12/310}},
doi = {{10.1088/0305-4470/36/12/310}},
volume = {{36}},
year = {{2003}},
}