Time series analysis of the developed financial markets' integration using visibility graphs

Abstract A time series representing the developed financial markets’ segmentation from 1973 to 2012 is studied. The time series reveals an obvious market integration trend. To further uncover the features of this time series, we divide it into seven windows and generate seven visibility graphs. The measuring capabilities of the visibility graphs provide means to quantitatively analyze the original time series. It is found that the important historical incidents that influenced market integration coincide with variations in the measured graphical node degree. Through the measure of neighborhood span, the frequencies of the historical incidents are disclosed. Moreover, it is also found that large “cycles” and significant noise in the time series are linked to large and small communities in the generated visibility graphs. For large cycles, how historical incidents significantly affected market integration is distinguished by density and compactness of the corresponding communities.

[1]  Zhong-Ke Gao,et al.  Erratum: “Complex network from time series based on phase space reconstruction” [Chaos 19, 033137 (2009)] , 2010 .

[2]  Geert Bekaert,et al.  Market Integration and Investment Barriers in Emerging Equity Markets , 1995 .

[3]  Campbell R. Harvey,et al.  Financial Openness and Productivity , 2009 .

[4]  Yue Yang,et al.  Complex network-based time series analysis , 2008 .

[5]  Muhammad Sahimi,et al.  Mapping stochastic processes onto complex networks , 2009 .

[6]  Richard Roll,et al.  Global market integration: An alternative measure and its application , 2009 .

[7]  Michael Small,et al.  Superfamily phenomena and motifs of networks induced from time series , 2008, Proceedings of the National Academy of Sciences.

[8]  Michael Small,et al.  Recurrence-based time series analysis by means of complex network methods , 2010, Int. J. Bifurc. Chaos.

[9]  B. Luque,et al.  Horizontal visibility graphs: exact results for random time series. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[11]  Campbell R. Harvey,et al.  What Segments Equity Markets? , 2009 .

[12]  M Small,et al.  Complex network from pseudoperiodic time series: topology versus dynamics. , 2006, Physical review letters.

[13]  J. Peydró,et al.  Financial Regulation, Financial Globalization and the Synchronization of Economic Activity , 2009, SSRN Electronic Journal.

[14]  Michael Small,et al.  Complex network approach to characterize the statistical features of the sunspot series , 2013, 1307.6280.

[15]  Geert Bekaert,et al.  What Segments Equity Markets? , 2011 .

[16]  Xiaoming Liu,et al.  SLPA: Uncovering Overlapping Communities in Social Networks via a Speaker-Listener Interaction Dynamic Process , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[17]  Zhongke Gao,et al.  Complex network from time series based on phase space reconstruction. , 2009, Chaos.

[18]  Lucas Lacasa,et al.  From time series to complex networks: The visibility graph , 2008, Proceedings of the National Academy of Sciences.