Correlation structure and dynamics of international real estate securities markets: A network perspective

In this paper, we investigate the correlation structure and dynamics of international real estate securities markets by using daily returns of 20 national markets during the period 2006–2012 from a network perspective. We construct the minimum spanning tree (MST), the hierarchical tree (HT), and the planar maximally filtered graph (PMFG) obtained from the correlation matrix computed by the daily returns during the investigated period, and analyze the corresponding clustering structure, hierarchical structure, and community structure. We also build the time-varying MST and PMFG networks by a rolling window to examine the dynamics of correlation structure. The empirical results show that (1) the distribution of correlation coefficients is asymmetric, fat-tailed, and non-Gaussian. (2) The distributions of the influence-strength of the MST and PMFG networks obey a power-law. (3) Two clusters (i.e., the European and Asia-Pacific clusters) are found in the MST network, three hierarchical clusters (i.e., two like in the MST and the North American cluster) in the HT, and three communities in the PMFG network, which shows that national markets are linked together according to their geographical distributions. (4) The descriptive statistics of correlation coefficients and distances of the MSTs and PMFGs are time-varying; especially during periods of crisis they have a large fluctuation. (5) A huge number of linkages between national markets survive from one time to the next, and the long-term stability of the correlation structure in international real estate securities markets descends as time goes on. Our obtained results are new insights in international real estate securities markets and have wide applications for investment portfolio and risk management.

[1]  Juan Gabriel Brida,et al.  Dynamics and Structure of the 30 Largest North American Companies , 2007 .

[2]  B. Case,et al.  Dynamic Correlations Among Asset Classes: REIT and Stock Returns , 2011 .

[3]  Fabrizio Lillo,et al.  Correlation, Hierarchies, and Networks in Financial Markets , 2008, 0809.4615.

[4]  Wei‐Xing Zhou.  Multifractal detrended cross-correlation analysis for two nonstationary signals. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  J. Kwapień,et al.  Physical approach to complex systems , 2012 .

[6]  Brian M. Lucey,et al.  An ever-closer union? Examining the evolution of linkages of European equity markets via minimum spanning trees , 2008 .

[7]  Peter Richmond.  A roof over your head; house price peaks in the UK and Ireland , 2007 .

[8]  Elaine Worzala,et al.  Investing in International Real Estate Stocks: A Review of the Literature , 2003 .

[9]  Stanisław Drożdż,et al.  Analysis of a network structure of the foreign currency exchange market , 2009, 0906.0480.

[10]  Martin Hoesli,et al.  Volatility Spillovers, Comovements and Contagion in Securitized Real Estate Markets , 2011 .

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

[12]  Richard D. Deveaux,et al.  Applied Smoothing Techniques for Data Analysis , 1999, Technometrics.

[13]  P. Wilson,et al.  Asymmetric Volatility, Correlation and Returns Dynamics between the U.S. And U.K. Securitized Real Estate Markets , 2006 .

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

[15]  Fabrizio Lillo,et al.  Sector identification in a set of stock return time series traded at the London Stock Exchange , 2005 .

[16]  William T. Shaw,et al.  Correlation structure and dynamics in volatile markets , 2010 .

[17]  Rosario N. Mantegna,et al.  Evolution of Correlation Structure of Industrial Indices of US Equity Markets , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[19]  David Zilberman,et al.  Correlations between biofuels and related commodities before and during the food crisis: A taxonomy perspective , 2012 .

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

[21]  The Predictable Outcome of Speculative House Price Peaks , 2012 .

[22]  K. Liow,et al.  Correlation and Volatility Dynamics in International Real Estate Securities Markets , 2009 .

[23]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[24]  K. Kaski,et al.  Dynamics of market correlations: taxonomy and portfolio analysis. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Jian Zhou,et al.  Tail Dependence in International Real Estate Securities Markets , 2012 .

[26]  Chi Xie,et al.  Dynamics of Foreign Exchange Networks: A Time-Varying Copula Approach , 2014 .

[27]  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 .

[28]  Resul Eryigit,et al.  Network structure of cross-correlations among the world market indices , 2009 .

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

[30]  Real Estate Price Peaks—A Comparative Overview , 2006, physics/0605133.

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

[32]  E. Ben-Jacob,et al.  Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market , 2010, PloS one.

[33]  C. Kuratowski.  Sur le problème des courbes gauches en Topologie , 1930 .

[34]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[35]  Anish Goorah.  Real Estate Risk Management with Copulas , 2007 .

[36]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[37]  Martin Hoesli,et al.  FACULTE DES SCIENCES ECONOMIQUES ET SOCIALES , 2011 .

[38]  Shou Chen,et al.  Statistical Properties of the Foreign Exchange Network atDifferent Time Scales: Evidence from DetrendedCross-Correlation Coefficient and Minimum Spanning Tree , 2013, Entropy.

[39]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[40]  Bayram Deviren,et al.  Topology of the correlation networks among major currencies using hierarchical structure methods , 2011 .

[41]  Peter Richmond,et al.  The Evolution of Interdependence in World Equity Markets: Evidence from Minimum Spanning Trees , 2006, physics/0607022.

[42]  Y. Tse,et al.  Returns transmission, value at risk, and diversification benefits in international REITs: evidence from the financial crisis , 2013 .

[43]  J. Kruskal.  On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[44]  J. Bouchaud,et al.  Noise Dressing of Financial Correlation Matrices , 1998, cond-mat/9810255.

[45]  Rosario N. Mantegna,et al.  Evolution of Worldwide Stock Markets, Correlation Structure and Correlation Based Graphs , 2011 .

[46]  M Tumminello,et al.  A tool for filtering information in complex systems. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[47]  Stanisław Drożdż,et al.  The bulk of the stock market correlation matrix is not pure noise , 2006 .

[48]  Jian Zhou.  Multiscale Analysis of International Linkages of REIT Returns and Volatilities , 2012 .

[49]  Rosario N. Mantegna,et al.  Book Review: An Introduction to Econophysics, Correlations, and Complexity in Finance, N. Rosario, H. Mantegna, and H. E. Stanley, Cambridge University Press, Cambridge, 2000. , 2000 .

[50]  K. Liow,et al.  The Risk and Return Profile of Asian Real Estate Stocks , 2006 .

[51]  Stanisław Drożdż,et al.  Dynamics of competition between collectivity and noise in the stock market , 2000 .

[52]  V. Plerou,et al.  Universal and Nonuniversal Properties of Cross Correlations in Financial Time Series , 1999, cond-mat/9902283.

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

[54]  Leonidas Junior Sandoval.  Cluster formation and evolution in networks of financial market indices , 2013 .

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

[56]  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.

[57]  Chi Xie,et al.  The stability of financial market networks , 2014 .

[58]  S. Drożdż,et al.  Towards identifying the world stock market cross-correlations: DAX versus Dow Jones , 2000, cond-mat/0011488.

[59]  Nivedita Deo,et al.  Correlation and network analysis of global financial indices. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[60]  Leonidas Sandoval Junior.  Cluster formation and evolution in networks of financial market indices , 2011, Algorithmic Finance.

[61]  Brian M. Lucey,et al.  Comovements in government bond markets: A minimum spanning tree analysis , 2010 .