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The European Physical Journal B

, Volume 76, Issue 4, pp 529–535 | Cite as

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
Focus Section on Applications of Physics in Financial Analysis
  • 81 Downloads

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).

Keywords

Bipartite Graph Foreign Exchange Market Bipartite Network Poisson Random Variable Multiple Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of Applied Mathematics and PhysicsGraduate School of Informatics, Kyoto UniversityKyotoJapan
  2. 2.Graduate School of Business Administration, Keio UniversityYokohama KanagawaJapan

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