A new method to estimate the noise in financial correlation matrices

Thomas Guhr, Bernd Kalber

Research output: Contribution to journalArticlepeer-review

55 Citations (SciVal)


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.
Original languageEnglish
Pages (from-to)3009-3032
JournalJournal of Physics A: Mathematical and General
Issue number12
Publication statusPublished - 2003

Bibliographical note

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)

Subject classification (UKÄ)

  • Physical Sciences


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