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Statistical validation of financial time series via visibility graph

Author

Listed:
  • Matteo Serafino
  • Andrea Gabrielli
  • Guido Caldarelli
  • Giulio Cimini
Abstract
Statistical physics of complex systems exploits network theory not only to model, but also to effectively extract information from many dynamical real-world systems. A pivotal case of study is given by financial systems: market prediction represents an unsolved scientific challenge yet with crucial implications for society, as financial crises have devastating effects on real economies. Thus, nowadays the quest for a robust estimator of market efficiency is both a scientific and institutional priority. In this work we study the visibility graphs built from the time series of several trade market indices. We propose a validation procedure for each link of these graphs against a null hypothesis derived from ARCH-type modeling of such series. Building on this framework, we devise a market indicator that turns out to be highly correlated and even predictive of financial instability periods.

Suggested Citation

  • Matteo Serafino & Andrea Gabrielli & Guido Caldarelli & Giulio Cimini, 2017. "Statistical validation of financial time series via visibility graph," Papers 1710.10980, arXiv.org.
    • Handle: RePEc:arx:papers:1710.10980
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    File URL: http://arxiv.org/pdf/1710.10980
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