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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 positive correlations within the clusters and negative correlations between them. 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.

Suggested Citation

  • Fricke, Daniel, 2012. "Trading strategies in the overnight money market: Correlations and clustering on the e-MID trading platform," Kiel Working Papers 1766, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwkwp:1766
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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.
    2. Ivan Alves & Stijn Ferrari & Pietro Franchini & Jean-Cyprien Heam & Pavol Jurca & Sam Langfield & Sebastiano Laviola & Franka Liedorp & Antonio Sánchez & Santiago Tavolaro & Guillaume Vuillemey, 2013. "The structure and resilience of the European interbank market," ESRB Occasional Paper Series 03, European Systemic Risk Board.
    3. Luu, Duc Thi & Lux, Thomas & Yanovski, Boyan, 2017. "Structural correlations in the Italian overnight money market: An analysis based on network configuration models," Economics Working Papers 2017-02, Christian-Albrechts-University of Kiel, Department of Economics.
    4. Fabio Vanni & Paolo Barucca, 2017. "Time evolution of an agent-driven network model," LEM Papers Series 2017/16, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    5. Fabio Vanni & Paolo Barucca, 2019. "Degree-correlations in a bursting dynamic network model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(3), pages 663-695, September.
    6. Nicolò Pecora & Alessandro Spelta, 2016. "Discovering SIFIs in interbank communities," DISCE - Working Papers del Dipartimento di Economia e Finanza def037, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    7. Morteza Alaeddini & Philippe Madiès & Paul J. Reaidy & Julie Dugdale, 2023. "Interbank money market concerns and actors’ strategies—A systematic review of 21st century literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 573-654, April.
    8. Nicolò Pecora & Pablo Rovira Kaltwasser & Alessandro Spelta, 2016. "Discovering SIFIs in Interbank Communities," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-17, December.
    9. Markus Engler & Vahidin Jeleskovic, 2016. "Intraday volatility, trading volume and trading intensity in the interbank market e-MID," MAGKS Papers on Economics 201648, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    10. Pablo Rovira Kaltwasser & Alessandro Spelta, 2019. "Identifying systemically important financial institutions: a network approach," Computational Management Science, Springer, vol. 16(1), pages 155-185, February.
    11. Martin D. Gould & Nikolaus Hautsch & Sam D. Howison & Mason A. Porter, 2020. "Counterparty Credit Limits: The Impact of a Risk-Mitigation Measure on Everyday Trading," Applied Mathematical Finance, Taylor & Francis Journals, vol. 27(6), pages 520-548, November.
    12. Pecora, Nicolò & Spelta, Alessandro, 2015. "Shareholding relationships in the Euro Area banking market: A network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 1-12.
    13. Duc Thi Luu, 2022. "Portfolio Correlations in the Bank-Firm Credit Market of Japan," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 529-569, August.
    14. Lux, Thomas, 2016. "Network effects and systemic risk in the banking sector," FinMaP-Working Papers 62, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    15. Anastasios Demertzidis, 2019. "Interbank transactions on the intraday frequency: -Different market states and the effects of the financial crisis-," MAGKS Papers on Economics 201932, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    16. Gould, Martin D. & Hautsch, Nikolaus & Howison, Sam D. & Porter, Mason A., 2017. "Counterparty credit limits: An effective tool for mitigating counterparty risk?," CFS Working Paper Series 581, Center for Financial Studies (CFS).

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    More about this item

    Keywords

    interbank market; socio-economic networks; community identification;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System

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