dorsal/arxiv
View SchemaAsymmetric matrices in an analysis of financial correlations
| Authors | J. Kwapien, S. Drozdz, A. Z. Gorski, P. Oswiecimka |
|---|---|
| Categories | |
| ArXiv ID | physics/0605115 |
| URL | https://arxiv.org/abs/physics/0605115 |
| Journal | Acta Phys. Pol. B 37, 3039-3048 (2006) |
Abstract
Financial markets are highly correlated systems that reveal both the inter-market dependencies and the correlations among their different components. Standard analyzing techniques include correlation coefficients for pairs of signals and correlation matrices for rich multivariate data. In the latter case one constructs a real symmetric matrix with real non-negative eigenvalues describing the correlation structure of the data. However, if one performs a correlation-function-like analysis of multivariate data, when a stress is put on investigation of delayed dependencies among different types of signals, one can calculate an asymmetric correlation matrix with complex eigenspectrum. From the Random Matrix Theory point of view this kind of matrices is closely related to Ginibre Orthogonal Ensemble (GinOE). We present an example of practical application of such matrices in correlation analyses of empirical data. By introducing the time lag, we are able to identify temporal structure of the inter-market correlations. Our results show that the American and German stock markets evolve almost simultaneously without a significant time lag so that it is hard to find imprints of information transfer between these markets. There is only an extremely subtle indication that the German market advances the American one by a few seconds.
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"abstract": "Financial markets are highly correlated systems that reveal both the\ninter-market dependencies and the correlations among their different\ncomponents. Standard analyzing techniques include correlation coefficients for\npairs of signals and correlation matrices for rich multivariate data. In the\nlatter case one constructs a real symmetric matrix with real non-negative\neigenvalues describing the correlation structure of the data. However, if one\nperforms a correlation-function-like analysis of multivariate data, when a\nstress is put on investigation of delayed dependencies among different types of\nsignals, one can calculate an asymmetric correlation matrix with complex\neigenspectrum. From the Random Matrix Theory point of view this kind of\nmatrices is closely related to Ginibre Orthogonal Ensemble (GinOE). We present\nan example of practical application of such matrices in correlation analyses of\nempirical data. By introducing the time lag, we are able to identify temporal\nstructure of the inter-market correlations. Our results show that the American\nand German stock markets evolve almost simultaneously without a significant\ntime lag so that it is hard to find imprints of information transfer between\nthese markets. There is only an extremely subtle indication that the German\nmarket advances the American one by a few seconds.",
"arxiv_id": "physics/0605115",
"authors": [
"J. Kwapien",
"S. Drozdz",
"A. Z. Gorski",
"P. Oswiecimka"
],
"categories": [
"physics.soc-ph",
"q-fin.ST"
],
"journal_ref": "Acta Phys. Pol. B 37, 3039-3048 (2006)",
"title": "Asymmetric matrices in an analysis of financial correlations",
"url": "https://arxiv.org/abs/physics/0605115"
},
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