Topology structure based on detrended cross-correlation coefficient of exchange rate network of the belt and road countries

The Belt and Road initiative has been gaining attention internationally since its proposal. This study applies complex network theory to the Belt and Road countries’ exchange rate markets by constructing a correlation network for these markets using the detrended cross-correlation coefficient (DCCA cross-correlation coefficient). Results show that the Belt and Road countries’ exchange rate network (BREN)11Hereinafter referred to as BREN.exhibits a small-world effect and robustness. The network is divided into three clusters by factional analysis. The three clusters correspond to three regions: West Asia, Central Asia and Europe, and Southeast Asia. The cohesion subgroup density between Central Asia and Europe and West Asia is high, and the inter-correlation of the Central Asia and Europe is strong. Moreover, the CNY’s position in the BREN has been significantly improved since the policy was proposed.

[1]  Chen-hua Shen,et al.  Analysis of detrended time-lagged cross-correlation between two nonstationary time series , 2015 .

[2]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[3]  Ling-Yun He,et al.  A new approach to quantify power-law cross-correlation and its application to commodity markets , 2011 .

[4]  Byungnam Kahng,et al.  Weighted Scale-Free Network in Financial Correlations , 2002 .

[5]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Hu Ping,et al.  The characteristics analysis of the stock network based on weighted relative values: An example of information service industry , 2012, 2012 First National Conference for Engineering Sciences (FNCES 2012).

[7]  Chi Xie,et al.  Random matrix theory analysis of cross-correlations in the US stock market: Evidence from Pearson’s correlation coefficient and detrended cross-correlation coefficient , 2013 .

[8]  Wei Wang,et al.  Simulation Research of Space-Time Evolution of Emergency Logistics Network Reliability Based on Complex Network Theory , 2013 .

[9]  Panos M. Pardalos,et al.  Statistical analysis of financial networks , 2005, Comput. Stat. Data Anal..

[10]  Xintian Zhuang,et al.  A network analysis of the Chinese stock market , 2009 .

[11]  K. Kaski,et al.  Clustering and information in correlation based financial networks , 2003, cond-mat/0312682.

[12]  X. Wen,et al.  Housing demand or money supply? A new Keynesian dynamic stochastic general equilibrium model on China’s housing market fluctuations , 2015 .

[13]  Petre Caraiani,et al.  Characterizing emerging European stock markets through complex networks: From local properties to self-similar characteristics , 2012 .

[14]  Ladislav Kristoufek,et al.  Measuring correlations between non-stationary series with DCCA coefficient , 2013, 1310.3984.

[15]  R. Mantegna Hierarchical structure in financial markets , 1998, cond-mat/9802256.

[16]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[17]  G. F. Zebende,et al.  DCCA cross-correlation coefficient differentiation: Theoretical and practical approaches , 2013 .

[18]  Jaehwa Kim,et al.  Cross Correlation Analysis of Gamma Exposure Rates and Rainfall, Hours of Saylight, Average Wind Speed in Gangneung Area , 2013 .

[19]  Chi Xie,et al.  Detrended minimum-variance hedge ratio: A new method for hedge ratio at different time scales , 2014 .

[20]  Benjamin Miranda Tabak,et al.  Topological properties of stock market networks: The case of Brazil , 2010 .

[21]  Chi Xie,et al.  Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree , 2012 .

[22]  H. Stanley,et al.  Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series , 2002, physics/0202070.

[23]  H. Stanley,et al.  Quantifying cross-correlations using local and global detrending approaches , 2009 .

[24]  Francis C. M. Lau,et al.  A network perspective of the stock market , 2010 .

[25]  H. Stanley,et al.  Cross-correlations between volume change and price change , 2009, Proceedings of the National Academy of Sciences.

[26]  Gilney Figueira Zebende,et al.  Oil and US dollar exchange rate dependence: A detrended cross-correlation approach , 2014 .

[27]  G. F. Zebende,et al.  Why does the euro fail? The DCCA approach , 2016 .

[28]  T. Aste,et al.  Correlation based networks of equity returns sampled at different time horizons , 2007 .

[29]  J. Kwapień,et al.  Detrended fluctuation analysis made flexible to detect range of cross-correlated fluctuations. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  S. Havlin,et al.  Self-similarity of complex networks , 2005, Nature.

[31]  Ling-Yun He,et al.  Multifractal Detrended Cross-Correlation Analysis of agricultural futures markets , 2011 .

[32]  Zbigniew R. Struzik,et al.  Structural and topological phase transitions on the German Stock Exchange , 2013, 1301.2530.

[33]  X. Wen,et al.  PREDICTABILITY AND MARKET EFFICIENCY IN AGRICULTURAL FUTURES MARKETS: A PERSPECTIVE FROM PRICE-VOLUME CORRELATION BASED ON WAVELET COHERENCY ANALYSIS , 2015 .

[34]  Chi Xie,et al.  Cross-correlations between Renminbi and four major currencies in the Renminbi currency basket , 2013 .

[35]  Ladislav Kristoufek,et al.  Detrending moving-average cross-correlation coefficient: Measuring cross-correlations between non-stationary series , 2013, 1311.0657.

[36]  Y. Shao,et al.  Is the efficiency of stock market correlated with multifractality? An evidence from the Shanghai stock market , 2013 .

[37]  Y. Si,et al.  A detrended cross-correlation analysis of meteorological and API data in Nanjing, China , 2015 .

[38]  Wenjuan Xie,et al.  Contemporaneous and Asymmetric Properties in the Price-Volume Relationships in China's Agricultural Futures Markets , 2014 .

[39]  Jun Wang,et al.  Statistical analysis on multifractal detrended cross-correlation coefficient for return interval by oriented percolation , 2015 .

[40]  Ling-Yun He,et al.  Bubble Formation and Heterogeneity of Traders: A Multi-Agent Perspective , 2013 .

[41]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[42]  H. Stanley,et al.  Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. , 2007, Physical review letters.

[43]  Boris Podobnik,et al.  Statistical tests for power-law cross-correlated processes. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[44]  Chi Xie,et al.  Are stock market networks non-fractal? Evidence from New York Stock Exchange , 2016 .

[45]  Zuntao Fu,et al.  Different spatial cross-correlation patterns of temperature records over China: A DCCA study on different time scales , 2014 .

[46]  I.-M. Kim,et al.  Scale-Free Network in Stock Markets , 2002 .

[47]  G. F. Zebende DCCA cross-correlation coefficient: Quantifying level of cross-correlation , 2011 .

[48]  Guangxi Cao,et al.  Detrended cross-correlation analysis approach for assessing asymmetric multifractal detrended cross-correlations and their application to the Chinese financial market , 2014 .

[49]  Jian Lu,et al.  Weighted Complex Network Analysis of Shanghai Rail Transit System , 2016 .

[50]  Siew Lee Gan,et al.  Optimality problem of network topology in stocks market analysis , 2015 .