Graph-based era segmentation of international financial integration

Abstract

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 ; 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.

Another history of global financial markets: Local stock market integration since 1913 from a network perspective

“…We analyse long‐run stock market integration by computing rolling windows of the distance and connectivity network indicators. The integration measures are calculated by replicating the construction of minimal spanning trees over rolling periods of 100 data points, starting from the beginning of the sample period in 1913 (Bastidon et al, 2020). The method produces time series of network indicators, where each point corresponds to the structure of the network over the previous 100 months.…”

“…This process accelerates in the past three decades. At the same time, the nature of international monetary relations influences network structures, and thus connectivity indicators (e.g., Wójcik, 2009 for a geographic perspective, or Bastidon et al, 2020 for the dominant effect of institutional events on the segmentation of dynamic financial networks). A system favourable to international financial transactions (first era of globalisation) is associated with a predominant star structure; an unfavourable system (interwar) with a regression of star structures; and a system that is again more favourable (Bretton‐Woods) and then largely deregulated (post‐1980) with a star structure that is again increasingly predominant.…”

The Contours of a Cliophysics. How Can Econophysics Enrich Cliometrics? Case Studies in Debt Issues and Global Capital Markets

“…To identify endogenous areas of globalization based on homogeneous structures, [3] use a sample including monthly data on 32 national equity markets from 1960 to 2018 (source: OECD). The identification of endogenous eras is based on signal processing to segment the time series of network indicators into stationary periods.…”

“…The work carried out jointly by physicists and economists [1][2][3] within the framework of the CAC team (hosted by IXXI, Institut Rhône Alpin des Systémes Complexes of ENS de Lyon) aims at renewing the methodological approach to major cliometric subjects: the public debt -growth relationship over history [1]; the development of financial globalization [3]; and the detection of major events in global capital markets [2]. On these three topics, the dating and/or identification of homogeneous periods is a major challenge of the empirical approach.…”

“…Applied to time series of debt [1], global asset prices correlations [2] and graph indicators based on correlations [3], these signal processing methods shed light on central controversies. In particular, this work shows, for the post-World War II period, that the debt growth relationship cannot be considered in its sole domestic dimension [1]; that decisions of market operators are the main cause of major breaks in the dynamics of financial globalization [3]; and that the dependence of asset price time series allows for a satisfactory dating of major events in global capital markets [2].…”