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Robust Statistical Procedures for Testing Dynamics in Market Network

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Book cover Computational Aspects and Applications in Large-Scale Networks (NET 2016)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 247))

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Abstract

Market network analysis attracts a growing attention last decades. One of the most important problems related with it is the detection of dynamics in market network. In the present paper, the stock market network of stock’s returns is considered. Probability of sign coincidence of stock’s returns is used as the measure of similarity between stocks. Robust (distribution free) multiple testing statistical procedure for testing dynamics of network is proposed. The constructed procedure is applied for German, French, UK, and USA market. It is shown that in most cases where the dynamics is observed it is determined by a small number of hubs in the associated rejection graph.

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References

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Acknowledgements

The work is partially supported by RFHR grant 15-32-01052.

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Authors and Affiliations

  1. Laboratory of Algorithms and Technologies for Network Analysis, National Research University Higher School of Economics, Nizhny Novgorod, Russia

    A. P. Koldanov & M. A. Voronina

Authors
  1. A. P. Koldanov
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  2. M. A. Voronina
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Corresponding author

Correspondence to M. A. Voronina .

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Editors and Affiliations

  1. Higher School of Economics, National Research University, Nizhny Novgorod, Russia

    Valery A. Kalyagin

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  3. Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Oleg Prokopyev

  4. Higher School of Economics, National Research University, Nizhny Novgorod, Russia

    Irina Utkina

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Koldanov, A.P., Voronina, M.A. (2018). Robust Statistical Procedures for Testing Dynamics in Market Network. In: Kalyagin, V., Pardalos, P., Prokopyev, O., Utkina, I. (eds) Computational Aspects and Applications in Large-Scale Networks. NET 2016. Springer Proceedings in Mathematics & Statistics, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-96247-4_9

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