PL EN


Preferences help
enabled [disable] Abstract
Number of results
2015 | 127 | 3 | 863-867
Article title

Partial Mutual Information Analysis of Financial Networks

Authors
Content
Title variants
Languages of publication
EN
Abstracts
EN
The econophysics approach to socio-economic systems is based on the assumption of their complexity. Such assumption inevitably leads to another assumption, namely that underlying interconnections within socio-economic systems, particularly financial markets, are nonlinear, which is shown to be true even in mainstream economic literature. Thus it is surprising to see that network analysis of financial markets is based on linear correlation and its derivatives. An analysis based on partial correlation is of particular interest as it leads to the vicinity of causality detection in time series analysis. In this paper we generalise the planar maximally filtered graphs and partial correlation planar graphs to incorporate nonlinearity using partial mutual information.
Keywords
EN
Contributors
author
  • Cracow University of Economics, Rakowicka 27, 31-510 Kraków, Poland
References
  • [1] H. Markowitz, J. Financ. 7, 77 (1952), doi: 10.2307/2975974
  • [2] R. Mantegna, Eur. Phys. J. B 11, 193 (1999), doi: 10.1007/s100510050929
  • [3] P. Cizeau, M. Potters, J. Bouchaud, Quant. Financ. 1, 217 (2001), doi: 10.1088/1469-7688/1/2/303
  • [4] K. Forbes, R. Rigobon, J. Financ. 57, 2223 (2002), doi: 10.1111/0022-1082.00494
  • [5] B. Podobnik, H. Stanley, Phys. Rev. Lett. 100, 084102 (2008), doi: 10.1103/PhysRevLett.100.084102
  • [6] T. Aste, W. Shaw, T.D. Matteo, New J. Phys. 12, 085009 (2010), doi: 10.1088/1367-2630/12/8/085009
  • [7] D. Kenett, T. Preis, G. Gur-Gershgoren, E. Ben-Jacob, Europhys. Lett. 99, 38001 (2012), doi: 10.1209/0295-5075/99/38001
  • [8] G. Bonanno, F. Lillo, R. Mantegna, Quant. Financ. 1, 96 (2001), doi: 10.1088/1469-7688/1/1/306
  • [9] M. Tumminello, T.D. Matteo, T. Aste, R. Mantegna, Eur. Phys. J. B 55, 209 (2007), doi: 10.1140/epjb/e2006-00414-4
  • [10] M. Munnix, R. Schafer, T. Guhr, Physica A 389, 4828 (2010), doi: 10.1016/j.physa.2010.06.037
  • [11] G. Bonanno, N. Vandewalle, R.N. Mantegna, Phys. Rev. E 62, 7615 (2000), doi: 10.1103/PhysRevE.62.R7615
  • [12] S. Maslov, Physica A 301, 397 (2001), doi: 10.1016/S0378-4371(01)00370-3
  • [13] S. Drożdż, F. Grummer, F. Ruf, J. Speth, Physica A 294, 226 (2001), doi: 10.1016/S0378-4371(01)00119-4
  • [14] R. Coelho, C. Gilmore, B. Lucey, P. Richmond, S. Hutzler, Physica A 376, 455 (2007), doi: 10.1016/j.physa.2006.10.045
  • [15] C.G. Gilmore, B.M. Lucey, M. Boscia, Physica A 387, 6319 (2008), doi: 10.1016/j.physa.2008.07.012
  • [16] M. Eryigit, R. Eryigit, Physica A 388, 3551 (2009), doi: 10.1016/j.physa.2009.04.028
  • [17] D.M. Song, M. Tumminello, W.X. Zhou, R.N. Mantegna, Phys. Rev. E 84, 026108 (2011), doi: 10.1103/PhysRevE.84.026108
  • [18] L. Sandoval, I. Franca, Physica A 391, 187 (2012), doi: 10.1016/j.physa.2011.07.023
  • [19] M. McDonald, O. Suleman, S. Williams, S. Howison, N.F. Johnson, Phys. Rev. E 72, 046110 (2005), doi: 10.1103/PhysRevE.72.046106
  • [20] R.N. Mantegna, H.E. Stanley, Introduction to Econophysics: Correlations and Complexity in Finance, Cambridge University Press, Cambridge 1999, doi: 10.1017/CBO9780511755767
  • [21] B. Rosser, Adv. Complex Syst. 11, 745 (2008), doi: 10.1142/S0219525908001957
  • [22] W.A. Brock, D.A. Hsieh, B. LeBaron, Nonlinear Dynamics, Chaos, and Instability. Statistical Theory and Economic Evidence, MIT Press, Cambridge 1991
  • [23] M. Qi, J. Bus. Econ. Stat. 17, 419 (1999), doi: 10.2307/1392399
  • [24] D. McMillan, Int. Rev. Econ. Financ. 10, 353 (2001), doi: 10.1016/S1059-0560(01)00093-4
  • [25] D. Sornette, J. Andersen, Int. J. Mod. Phys. C 13, 171 (2002), doi: 10.1142/S0129183102003085
  • [26] K. Oh, K. Kim, Expert Syst. Appl. 22, 249 (2002), doi: 10.1016/S0957-4174(01)00058-6
  • [27] P.H. Franses, D.V. Dijk, J. Forecasting 15, 229 (1996), doi: 10.1002/(SICI)1099-131X(199604)15:3%3C229::AID-FOR620%3E3.3.CO;2-V
  • [28] A. Abhyankar, L. Copeland, W. Wong, Econ. J. 105, 864 (1995)
  • [29] P. Chen, Stud. Nonlinear Dyn. E. 1, 87 (1996), doi: 10.2202/1558-3708.1014
  • [30] A. Abhyankar, L. Copeland, W. Wong, J. Bus. Econ. Stat. 15, 1 (1997), doi: 10.1080/07350015.1997.10524681
  • [31] P.A. Ammermann, D.M. Patterson, Pac. Bas. Financ. J. 11, 175 (2003), doi: 10.1016/S0927-538X(02)00113-0
  • [32] D. Hsieh, J. Bus. 62, 339 (1989), doi: 10.1086/296466
  • [33] R. Meese, A. Rose, Rev. Econ. Stud. 58, 603 (1991), doi: 10.2307/2298014
  • [34] C. Brooks, Appl. Financ. Econ. 6, 307 (1996), doi: 10.1080/096031096334105
  • [35] M. Qi, Y. Wu, J. Empir. Financ. 10, 623 (2003), doi: 10.2307/1392399
  • [36] P. Fiedor, Phys. Rev. E 89, 052801 (2014), doi: 10.1103/PhysRevE.89.052801
  • [37] P. Fiedor, Eur. Phys. J. B 87, 168 (2014), doi: 10.1140/epjb/e2014-50108-3
  • [38] D. Kenett, M. Tumminello, A. Madi, G. Gur-Gershgoren, R. Mantegna, E. Ben-Jacob, PloS one 5, e15032 (2010), doi: 10.1371/journal.pone.0015032
  • [39] D. Kenett, X. Huang, I. Vodenska, S. Havlin, H. Stanley, arXiv:1402.1405 (2014)Quant. Financ. 15, 569 (2015), doi: 10.1080/14697688.2014.946660
  • [40] N. Navet, S.H. Chen, in: Natural Computing in Computational Finance, Eds. T. Brabazon, M. O'Neill, Vol. 100 of Studies in Computational Intelligence, Springer, Berlin-Heidelberg 2008, p. 197, doi: 10.1007/978-3-540-77477-8_11
  • [41] P. Fiedor, in: Proc. IEEE Computational Intelligence for Financial Engineering and Economics 2014, Eds.: A. Serguieva, D. Maringer, V. Palade, R.J. Almeida, IEEE, London 2014, p. 247, doi: 10.1109/CIFEr.2014.6924080
  • [42] C.E. Shannon, Bell Syst. Tech. J. 27, 379 (1948), doi: 10.1002/j.1538-7305.1948.tb01338.x
  • [43] L. Paninski, Neural Comput. 15, 1191 (2003), doi: 10.1162/089976603321780272
  • [44] C. Stevens, A. Zador, Advances in Neural Information Processing Systems 8, Eds.: D.S. Touretzky, M.C. Mozer, M.E. Hasselmo, MIT Press, Boston 1995, p. 75
  • [45] S. Strong, R. Koberle, R. de Ruyter van Steveninck, W. Bialek, Phys. Rev. Lett. 80, 197 (1998), doi: 10.1103/PhysRevLett.80.197
  • [46] P. Reinagel, Curr. Biol. 10, 542 (2000), doi: 10.1016/S0960-9822(00)00609-6
  • [47] W. Nemenman, W. Bialek, R. de Ruyter van Steveninck, Phys. Rev. E 69, 056111 (2004), doi: 10.1103/PhysRevE.69.056111
  • [48] D. Warland, P. Reinagel, M. Meister, J. Neurophysiol. 78, 2336 (1997)
  • [49] J. Bonachela, H. Hinrichsen, M. Munoz, J. Phys. A Math. Theor. 41, 202001 (2008), doi: 10.1088/1751-8113/41/20/202001
  • [50] T. Schurmann, P. Grassberger, Chaos 6, 414 (1996), doi: 10.1063/1.166191
  • [51] D. Kugiumtzis, Eur. Phys. J. Spec. Top. 222, 401 (2013), doi: 10.1140/epjst/e2013-01849-4
  • [52] K. Hlavackova-Schindler, M. Palus, M. Vejmelka, J. Bhattacharya, Phys. Rep. 441, 1 (2007), doi: 10.1016/j.physrep.2006.12.004
  • [53] C. Curme, M. Tumminello, R. Mantegna, H. Stanley, D. Kenett, arXiv:1401.0462, (2014)
  • [54] H. Jeong, S.P. Mason, A.L. Barabasi, Z.N. Oltvai, Nature 411, 41 (2001), doi: 10.1038/35075138
  • [55] S. White, P. Smyth, in: Proc. Ninth ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Eds.: L. Getoor, T. Senator, P. Domingos, Ch. Faloutsos, ACM, New York 2003, p. 266, doi: 10.1145/956750.956782
  • [56] U. Brandes, T. Erlebach, Network Analysis: Methodological Foundations, Lecture Notes in Computer Science, Springer, Berlin-Heidelberg 2005, doi: 10.1007/b106453
  • [57] G. Bonanno, G. Caldarelli, F. Lillo, R. Mantegna, Phys. Rev. E 68, 046130 (2003), doi: 10.1103/PhysRevE.68.046130
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.bwnjournal-article-appv127n338kz
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.