@article{serafino2017statistical,
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.},
archivePrefix = {arXiv},
arxivId = {1710.10980},
author = {Serafino, Matteo and Gabrielli, Andrea and Caldarelli, Guido and Cimini, Giulio},
eprint = {1710.10980},
journal = {Working Paper arXiv:1710.10980},
keywords = {DOLFINS{\_}T1.4,DOLFINS{\_}WP1,DOLFINS{\_}working{\_}paper},
mendeley-tags = {DOLFINS{\_}T1.4,DOLFINS{\_}WP1,DOLFINS{\_}working{\_}paper},
month = {oct},
title = {{Statistical validation of financial time series via visibility graph}},
url = {http://arxiv.org/abs/1710.10980},
year = {2017}
}

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