PhysRevE.84.016113.pdf (519.36 kB)
Fine structure of spectral properties for random correlation matrices: an application to financial markets
journal contribution
posted on 2023-06-08, 18:25 authored by Giacomo Livan, Simone Alfarano, Enrico ScalasWe study some properties of eigenvalue spectra of financial correlation matrices. In particular, we investigate the nature of the large eigenvalue bulks which are observed empirically, and which have often been regarded as a consequence of the supposedly large amount of noise contained in financial data. We challenge this common knowledge by acting on the empirical correlation matrices of two data sets with a filtering procedure which highlights some of the cluster structure they contain, and we analyze the consequences of such filtering on eigenvalue spectra. We show that empirically observed eigenvalue bulks emerge as superpositions of smaller structures, which in turn emerge as a consequence of cross-correlations between stocks. We interpret and corroborate these findings in terms of factor models, and and we compare empirical spectra to those predicted by Random Matrix Theory for such models.
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Publication status
- Published
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- Published version
Journal
Physical Review EISSN
1539-3755Publisher
American Physical SocietyExternal DOI
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84Page range
016113Department affiliated with
- Mathematics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2014-09-24First Open Access (FOA) Date
2014-09-24First Compliant Deposit (FCD) Date
2014-09-24Usage metrics
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