  Assessing world-wide financial integration constitutes a recurrent challenge
in macroeconometrics, often addressed by visual inspections searching for data
patterns. Econophysics literature enables us to build complementary,
data-driven measures of financial integration using graphs. The present
contribution investigates the potential and interests of a novel 3-step
approach that combines several state-of-the-art procedures to i) compute
graph-based representations of the multivariate dependence structure of asset
prices time series representing the financial states of 32 countries world-wide
(1955-2015); ii) compute time series of 5 graph-based indices that characterize
the time evolution of the topologies of the graph; iii) segment these time
evolutions in piece-wise constant eras, using an optimization framework
constructed on a multivariate multi-norm total variation penalized functional.
The method shows first that it is possible to find endogenous stable eras of
world-wide financial integration. Then, our results suggest that the most
relevant globalization eras would be based on the historical patterns of global
capital flows, while the major regulatory events of the 1970s would only appear
as a cause of sub-segmentation.
