  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.
