Dependency Relations among International Stock Market Indices
Tóm tắt
We develop networks of international stock market indices using information and correlation based measures. We use 83 stock market indices of a diversity of countries, as well as their single day lagged values, to probe the correlation and the flow of information from one stock index to another taking into account different operating hours. Additionally, we apply the formalism of partial correlations to build the dependency network of the data, and calculate the partial Transfer Entropy to quantify the indirect influence that indices have on one another. We find that Transfer Entropy is an effective way to quantify the flow of information between indices, and that a high degree of information flow between indices lagged by one day coincides to same day correlation between them.
Từ khóa
Tài liệu tham khảo
Sandoval, 2014, Structure of a Global Network of Financial Companies based on Transfer Entropy, Entropy, 16, 4443, 10.3390/e16084443
Sandoval, 2012, Correlation of financial markets in times of crisis, Phys. A, 391, 187, 10.1016/j.physa.2011.07.023
Sandoval, 2014, To lag or not to lag? How to compare indices of stock markets that operate at different times, Phys. A, 403, 227, 10.1016/j.physa.2014.02.039
Kenett, 2010, Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market, PLoS ONE, 5, e15032, 10.1371/journal.pone.0015032
Schreiber, 2000, Measuring information transfer, Phys. Rev. Lett., 85, 461, 10.1103/PhysRevLett.85.461
Granger, 1969, Investigating Causal Relations by Econometric Models and Cross-spectral Methods, Econometrica, 37, 424, 10.2307/1912791
Barnett, 2009, Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables, Phys. Rev. Lett., 103, 238701, 10.1103/PhysRevLett.103.238701
Marschinski, 2002, Analysing the information flow between financial time series-an improved estimator for Transfer Entropy, Eur. Phys. J. B, 30, 275, 10.1140/epjb/e2002-00379-2
Baek, S.K., Jung, W.-S., Kwon, O., and Moon, H.-T. Transfer Entropy Analysis of the Stock Market. Available online: http://arxiv.org/abs/physics/0509014.
Kwon, 2008, Information flow between composite stock index and individual stocks, Phys. A, 387, 2851, 10.1016/j.physa.2008.01.007
Kwon, 2008, Information flow between stock indices, Eur. Phys. Lett., 82, 68003, 10.1209/0295-5075/82/68003
Reddy, 2008, Interaction Between Forex and Stock Markets in India: An Entropy Approach, VIKALPA, 33, 27, 10.1177/0256090920080403
Jizba, 2012, Renyi’s information transfer between financial time series, Phys. A, 391, 2971, 10.1016/j.physa.2011.12.064
Peter, F.J., Dimpfl, T., and Huergo, L. Using Transfer Entropy to measure information flows from and to the CDS market. Available online: http://ssrn.com/abstract=1683948 or http://dx.doi.org/10.2139/ssrn.1683948.
Dimpfl, 2012, Using Transfer Entropy to measure information flows between financial markets, Stud. Nonlinear Dyn. Econ., 17, 85
Kim, 2013, Entropy-based analysis and bioinformatics-inspired integration of global economic information transfer, PLoS ONE, 8, e51986, 10.1371/journal.pone.0051986
Li, 2013, Risk contagion in Chinese banking industry: A Transfer Entropy-based analysis, Entropy, 15, 5549, 10.3390/e15125549
Dimpfl, 2014, The impact of the financial crisis on transatlantic information flows: An intraday analysis, J. Int. Financ. Mark. Inst. Money, 31, 1, 10.1016/j.intfin.2014.03.004
Sobaci, 2014, Effective Transfer Entropy Approach To Information Flow Between Exchange Rates And Stock Markets, Chaos Solitons Fractals, 68, 180, 10.1016/j.chaos.2014.08.007
Shapira, 2009, The index cohesive effect on stock market correlations, Eur. Phys. J. B-Condens. Matter Complex Syst., 72, 657, 10.1140/epjb/e2009-00384-y
Kenett, 2012, Correlations and Dependencies in the global financial village, Int. J. Mod. Phys. Conf. Ser., 16, 13, 10.1142/S201019451200774X
Kenett, 2012, Dependency network and node influence: Application to the study of financial markets, Int. J. Bifurc. Chaos, 22, 1250181, 10.1142/S0218127412501817
Madi, 2011, Analyses of antigen dependency networks unveil immune system reorganization between birth and adulthood, Chaos Interdiscip. J. Nonlinear Sci., 21, 016109, 10.1063/1.3543800
Kenett, 2011, Global and local features of semantic networks: Evidence from the Hebrew mental lexicon, PLoS ONE, 6, e23912, 10.1371/journal.pone.0023912
Dickey, 1979, Distribution of the Estimators for Autoregressive Time Series with a Unit Root, J. Am. Stat. Assoc., 74, 427
Phillips, 1998, Testing for a Unit Root in Time Series Regression, Biometrika, 75, 335, 10.1093/biomet/75.2.335
Kwiatkowski, 1992, Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root, J. Econ., 54, 159, 10.1016/0304-4076(92)90104-Y
Lo, 1988, Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test, Rev. Financ. Stud., 1, 41, 10.1093/rfs/1.1.41
Lo, 1989, The Size and Power of the Variance Ratio Test, J. Econ., 40, 203, 10.1016/0304-4076(89)90083-3
Shannon, 2001, A mathematical theory of communication, ACM SIGMOBILE Mob. Comput. Commun. Rev., 5, 3, 10.1145/584091.584093
Wibral, 2013, Measuring Information-Transfer Delays, PLoS ONE, 8, e55809+, 10.1371/journal.pone.0055809
Lizier, 2014, JIDT: An information-theoretic toolkit for studying the dynamics of complex systems, Front. Robot. AI, 1, 11, 10.3389/frobt.2014.00011
Sandoval, 2013, Cluster formation and evolution in networks of financial market indices, Algorithm. Financ., 2, 3, 10.3233/AF-13015
Borg, I., and Groenen, P. (2005). Modern Multidimensional Scaling: Theory and Applications, Springer. [2nd ed.].
Mantegna, 1999, Hierarchical structure in financial markets, Eur. Phys. J. B, 11, 193, 10.1007/s100510050929
Flavin, 2002, Explaining stock market correlation: A gravity model approach, Manch. Sch., 70, 87, 10.1111/1467-9957.70.s1.5
Bonanno, 2004, Networks of equities in financial markets, Eur. Phys. J. B, 38, 363, 10.1140/epjb/e2004-00129-6
Goo, Y.W., Lian, T.W., Ong, W.G., Choi, W.T., and Cheong, S.A. Financial atoms and molecules. Available online: http://arxiv.org/abs/0903.2099.
Coelho, 2007, The evolution of interdependence in world equity markets-evidence from minimum spanning trees, Phys. A, 376, 455, 10.1016/j.physa.2006.10.045
Eom, 2009, Topological properties of stock networks based on minimal spanning tree and random matrix theory in financial time series, Phys. A, 388, 900, 10.1016/j.physa.2008.12.006
2009, Network structure of cross-correlations among the world market indices, Phys. A, 388, 3551, 10.1016/j.physa.2009.04.028
Song, 2011, Evolution of worldwide stock markets, correlation structure and correlation based graphs, Phys. Rev. E, 84, 026108, 10.1103/PhysRevE.84.026108
Newman, M.E.J. (2010). Networks, and Introduction, Oxford University Press.
Mantegna, R.N., and Stanley, H.E. (2005). Introduction to Econophysics: Correlations and Complexity in Finance, Cambridge University Press.