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The multiplex dependency structure of financial markets


  • Nicol'o Musmeci
  • Vincenzo Nicosia
  • Tomaso Aste
  • Tiziana Di Matteo
  • Vito Latora


We propose here a multiplex network approach to investigate simultaneously different types of dependency in complex data sets. In particular, we consider multiplex networks made of four layers corresponding respectively to linear, non-linear, tail, and partial correlations among a set of financial time series. We construct the sparse graph on each layer using a standard network filtering procedure, and we then analyse the structural properties of the obtained multiplex networks. The study of the time evolution of the multiplex constructed from financial data uncovers important changes in intrinsically multiplex properties of the network, and such changes are associated with periods of financial stress. We observe that some features are unique to the multiplex structure and would not be visible otherwise by the separate analysis of the single-layer networks corresponding to each dependency measure.

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  • Nicol'o Musmeci & Vincenzo Nicosia & Tomaso Aste & Tiziana Di Matteo & Vito Latora, 2016. "The multiplex dependency structure of financial markets," Papers 1606.04872,
  • Handle: RePEc:arx:papers:1606.04872

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    Cited by:

    1. Tianyu Cui & Francesco Caravelli & Cozmin Ududec, 2017. "Correlations and Clustering in Wholesale Electricity Markets," Papers 1710.11184,, revised Nov 2017.
    2. Sindhuja Ranganathan & Mikko Kivela & Juho Kanniainen, 2017. "Dynamics of Investor Spanning Trees Around Dot-Com Bubble," Papers 1708.04430,

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