Fluctuation scaling and covariance matrix of constituents′ flows on a bipartite graph

Empirical analysis with high-frequency financial data based on a Poisson mixture model

A. H. Sato, T. Hayashi

Research output: Contribution to journalArticle

Abstract

We investigate an association between a power-law relationship of constituents′ flows (mean versus standard deviation) and their covariance matrix on a directed bipartite network. We propose a Poisson mixture model and a method to infer states of the constituents′ flows on such a bipartite network from empirical observation without a priori knowledge on the network structure. By using a proposed parameter estimation method with high frequency financial data we found that the scaling exponent and simultaneous cross-correlation matrix have a positive correspondence relationship. Consequently we conclude that the scaling exponent tends to be 1/2 in the case of desynchronous (specific dynamics is dominant), and to be 1 in the case of synchronous (common dynamics is dominant).

Original languageEnglish
Pages (from-to)529-535
Number of pages7
JournalEuropean Physical Journal B
Volume76
Issue number4
DOIs
Publication statusPublished - 2010 Aug

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Covariance matrix
scaling
exponents
Parameter estimation
cross correlation
standard deviation

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Electronic, Optical and Magnetic Materials

Cite this

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