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Trading strategies in the overnight money market: Correlations and clustering on the e-MID trading platform

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  • Fricke, Daniel

Abstract

We analyze the correlations in patterns of trading for members of the Italian interbank trading platform e-MID. The trading strategy of a particular member institution is defined as the sequence of (intra-) daily net trading volumes within a certain semester. Based on this definition, we show that there are significant and persistent bilateral correlations between institutions’ trading strategies. In most semesters we find two clusters, with positively (negatively) correlated trading strategies within (between) clusters. We show that the two clusters mostly contain continuous net buyers and net sellers of money, respectively, and that cluster memberships of individual banks are highly persistent. Additionally, we highlight some problems related to our definition of trading strategies. Our findings add further evidence on the fact that preferential lending relationships on the micro-level lead to community structure on the macro-level.

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Bibliographic Info

Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

Volume (Year): 391 (2012)
Issue (Month): 24 ()
Pages: 6528-6542

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Handle: RePEc:eee:phsmap:v:391:y:2012:i:24:p:6528-6542

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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

Related research

Keywords: Interbank market; Socio-economic networks; Community identification;

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References

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  1. G. Livan & S. Alfarano & E. Scalas, 2011. "The fine structure of spectral properties for random correlation matrices: an application to financial markets," Papers 1102.4076, arXiv.org.
  2. Michael Boss & Helmut Elsinger & Martin Summer & Stefan Thurner, 2004. "Network topology of the interbank market," Quantitative Finance, Taylor & Francis Journals, vol. 4(6), pages 677-684.
  3. Cocco, João F. & Gomes, Francisco J. & Martins, Nuno C., 2009. "Lending relationships in the interbank market," Journal of Financial Intermediation, Elsevier, vol. 18(1), pages 24-48, January.
  4. Iori, Giulia & De Masi, Giulia & Precup, Ovidiu Vasile & Gabbi, Giampaolo & Caldarelli, Guido, 2008. "A network analysis of the Italian overnight money market," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 259-278, January.
  5. Ilija I. Zovko & J. Doyne Farmer, 2007. "Correlations and clustering in the trading of members of the London Stock Exchange," Papers 0709.3261, arXiv.org.
  6. de Masi, G. & Iori, G. & Caldarelli, G., 2006. "A fitness model for the Italian interbank money market," Working Papers 06/08, Department of Economics, City University London.
  7. F. Kyriakopoulos & S. Thurner & C. Puhr & S. W. Schmitz, 2009. "Network and eigenvalue analysis of financial transaction networks," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, vol. 71(4), pages 523-531, October.
  8. Thomas Lux, Daniel Fricke, 2012. "Core-Periphery Structure in the Overnight Money Market: Evidence from the e-MID Trading Platform," Kiel Working Papers 1759, Kiel Institute for the World Economy.
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Cited by:
  1. Karl Finger & Daniel Fricke & Thomas Lux, 2013. "Network analysis of the e-MID overnight money market: the informational value of different aggregation levels for intrinsic dynamic processes," Computational Management Science, Springer, vol. 10(2), pages 187-211, June.

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