Analysis of Correlation Based Networks Representing DAX 30 Stock Price Returns

详细信息    查看全文

• 作者：Jenna Birch ; Athanasios A. Pantelous ; Kimmo Soramäki
• 关键词：MST ; PMFG ; AG ; DAX 30 ; Correlation networks ; European sovereign ; debt crisis
• 刊名：Computational Economics
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：47
• 期：4
• 页码：501-525
• 全文大小：1,928 KB
• 参考文献：Allen, F., & Gale, D. (2000). Financial contagion. Journal of Political Economy, 108, 1–33.CrossRef
  Aste, T., Di Matteo, T., Tumminello, M., & Mantegna, R. N. (2005). Correlation filtering in financial time series. Noise and fluctuations in econophysics and finance. Proceedings of SPIE, 5848, 100–109.CrossRef
  Becher, C., Millard, S., & Soramäki, K. (2008). The network topology of CHAPS Sterling. Working Paper No. 355. Bank of England.
  Birch, J., Pantelous, A. A., & Zuev, K. (2015). The maximum number of 3- and 4-cliques within a planar maximally filtered graph. Physica A, 417, 221–229.CrossRef
  Bonanno, G., Caldarelli, G., Lillo, F., Micciche, S., Vandewalle, N., & Mantegna, R. N. (2004). Networks of equities in financial markets. European Physical Journal B, 38, 363–371.CrossRef
  Bonanno, G., Vandewalle, N., & Mantegna, R. N. (2000). Taxonomy of stock market indices. Physical Review E, 62, R7615–R7618.CrossRef
  Boss, M., Elsinger, H., Summer, M., & Thurner, S. (2004). Network topology of the interbank market. Quantitative Finance, 4(6), 677–684.CrossRef
  Brookfield, D., Boussabaine, V., & Su, C. (2013). Identifying reference companies using the book-to-market ratio: A minimum spanning tree approach. The European Journal of Finance, 19(6), 466–490
  Galbiati, M., & Soramäki, K. (2012). Clearing networks. Journal of Economic Behavior and Organization, 83, 609–626.CrossRef
  Gower, J. C. (1966). Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53, 325–338.CrossRef
  Halinen, A., & Tornroos, J. (1998). The role of embeddedness in the evolution of business. Scandinavian Journal of Management, 14(3), 187–205.CrossRef
  Hopcroft, J., & Tarjan, R. (1974). Efficient planarity testing. Journal of the Association for Computing Machinery, 2(4), 549–568.CrossRef
  Iori, G., De Masi, G., Precup, O. V., Gabbi, G., & Caldarelli, G. (2008). A network analysis of the Italian overnight money market. Journal of Economic Dynamics and Control, 32, 259–278.CrossRef
  Kruskal, J. B. (1956). On the shortest spanning subtree of a graph and the travelling salesman problem. Proceedings of the American Mathematical Society, 7, 48–50.CrossRef
  Laloux, L., Cizeau, P., Bouchaud, J.-P., & Potters, M. (1999). Noise dressing of financial correlation matrices. Physical Review Letters, 83, 1467.CrossRef
  Leither, Y. (2005). Financial networks: Contagion, commitment, and private sector bailouts. Journal of Finance, 6(6), 2925–2953.CrossRef
  Li, S., He, J., & Zhuang, Y. (2010). A network model of the interbank market. Physica A, 389, 5587–5593.CrossRef
  Mantegna, R. N. (1999). Hierarchical structure in financial markets. European Physical Journal B, 11, 193–197.CrossRef
  Mizuno, T., Takayasu, H., & Takayasu, M. (2006). Correlation networks among currencies. Physica A, 364, 336–342.CrossRef
  Onnela, J.-P., Chakraborti, A., Kaski, K., & Kertesz, J. (2002). Dynamic asset trees and portfolio analysis. European Physical Journal B, 30, 285–288.CrossRef
  Onnela, J.-P., Chakraborti, A., Kaski, K., Kertesz, K., & Kanto, A. (2003). Asset trees and asset graphs in financial markets. Physica Scripta, T106, 48–54.CrossRef
  Prim, R. C. (1957). Shortest connection networks and some generalizations. Bell System Technical Journal, 36, 1389–1401.CrossRef
  Ringel, G. (1974). Map Color Theorem. Berlin: Springer.
  Schaeffer, S. E. (2007). Graph clustering. Computer Science Review, 1, 27–64.CrossRef
  Soramäki, K., Bech, M., Arnold, J., Glass, R. J., & Beyeler, R. J. (2007). The topology of interbank payment flows. Physica A, 379, 317–333.CrossRef
  Tse, C. K., Liu, J., & Lau, F. C. M. (2010). A network perspective of stock market. Journal of Empirical Finance, 17, 659–667.CrossRef
  Tumminello, M., Aste, T., Di Matteo, T., & Mantegna, R. N. (2005). A tool for filtering information in complex systems. Proceedings of the National Academy of Sciences, 102(30), 10421–10426.CrossRef
  Tumminello, M., Lillo, F., & Mantegna, R. N. (2010). Correlation, hierarchies and networks in financial markets. Journal of Economic Behavior and Organization, 75, 40–58.CrossRef
  Vandewalle, N., Brisbois, F., & Tordoir, X. (2001). Non-random topology of stock markets. Quantitative Finance, 1(3), 372–374.CrossRef
  
• 作者单位：Jenna Birch (1)
  Athanasios A. Pantelous (1) (2)
  Kimmo Soramäki (3)
  
  1. Institute for Financial and Actuarial Mathematics, Department of Mathematical Sciences, University of Liverpool, Liverpool, UK
  2. Institute for Risk and Uncertainty, University of Liverpool, Liverpool, UK
  3. Financial Network Analytics, London, UK
  
• 刊物类别：Business and Economics
• 刊物主题：Economics
  Economic Theory
  
• 出版者：Springer Netherlands
• ISSN：1572-9974

文摘

In this paper, we consider three methods for filtering pertinent information from a series of complex networks modelling the correlations between stock price returns of the DAX 30 stocks for the time period 2001–2012 using the Thomson Reuters Datastream database and also the FNA platform to create the visualizations of the correlation-based networks. These methods reduce the complete \(30\times 30\) correlation coefficient matrix to a simpler network structure consisting only of the most relevant edges. The chosen network structures include the minimum spanning tree, asset graph and the planar maximally filtered graph. The resulting networks and the extracted information are analysed and compared, looking at the clusters, cliques and connectivity. Finally, we consider some specific time periods (a) a period of crisis (October–December 2008) and (b) a period of recovery (May–August 2010) where we discuss the possible underlying economic reasoning for some aspects of the network structures produced. Overall, we find that network based representations of correlations within a broad market index are useful in providing insights about the growth dynamics of an economy.