IDEAS home Printed from https://ideas.repec.org/p/zbw/cauewp/201702.html
   My bibliography  Save this paper

Structural correlations in the Italian overnight money market: An analysis based on network configuration models

Author

Listed:
  • Luu, Duc Thi
  • Lux, Thomas
  • Yanovski, Boyan

Abstract

We study the structural correlations in the Italian overnight money market over the period 1999-2010. We show that the structural correlations vary across different versions of the network. Moreover, we employ different configuration models and examine whether higher-level characteristics of the observed network can be statistically reconstructed by maximizing the entropy of a randomized ensemble of networks restricted only by the lower-order features of the observed network. We find that often many of the high order correlations in the observed network can be considered emergent from the information embedded in the degree sequence in the binary version and in both the degree and strength sequences in the weighted version. However, this information is not enough to allow the models to account for all the patterns in the observed higher order structural correlations. In particular, one of the main features of the observed network that remains unexplained is the abnormally high level of weighted clustering in the years preceding the crisis, i.e. the huge increase in various indirect exposures generated via more intensive interbank credit links.

Suggested Citation

  • 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.
  • Handle: RePEc:zbw:cauewp:201702
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/156399/1/88320620X.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. D. Garlaschelli & T. Di Matteo & T. Aste & G. Caldarelli & M. I. Loffredo, 2007. "Interplay between topology and dynamics in the World Trade Web," Papers physics/0701030, arXiv.org.
    2. Zhang Bin & Horvath Steve, 2005. "A General Framework for Weighted Gene Co-Expression Network Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-45, August.
    3. Daniel Fricke & Thomas Lux, 2015. "On the distribution of links in the interbank network: evidence from the e-MID overnight money market," Empirical Economics, Springer, vol. 49(4), pages 1463-1495, December.
    4. Tiziano Squartini & Giorgio Fagiolo & Diego Garlaschelli, 2011. "Randomizing world trade. I. A binary network analysis," Papers 1103.1243, arXiv.org, revised Nov 2011.
    5. 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.
    6. 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.
    7. D. Garlaschelli & T. Di Matteo & T. Aste & G. Caldarelli & M. I. Loffredo, 2007. "Interplay between topology and dynamics in the World Trade Web," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 57(2), pages 159-164, May.
    8. Tabak, Benjamin M. & Takami, Marcelo & Rocha, Jadson M.C. & Cajueiro, Daniel O. & Souza, Sergio R.S., 2014. "Directed clustering coefficient as a measure of systemic risk in complex banking networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 211-216.
    9. Giulio Cimini & Tiziano Squartini & Diego Garlaschelli & Andrea Gabrielli, 2014. "Systemic risk analysis in reconstructed economic and financial networks," Papers 1411.7613, arXiv.org, revised May 2015.
    10. Tiziano Squartini & Giorgio Fagiolo & Diego Garlaschelli, 2011. "Randomizing world trade. II. A weighted network analysis," Papers 1103.1249, arXiv.org, revised Nov 2011.
    11. Holme, Petter & Min Park, Sung & Kim, Beom Jun & Edling, Christofer R., 2007. "Korean university life in a network perspective: Dynamics of a large affiliation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 821-830.
    12. Fricke, Daniel & Lux, Thomas, 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 (IfW Kiel).
    13. V. Zlatic & G. Bianconi & A. Díaz-Guilera & D. Garlaschelli & F. Rao & G. Caldarelli, 2009. "On the rich-club effect in dense and weighted networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(3), pages 271-275, February.
    14. Fricke, Daniel, 2012. "Trading strategies in the overnight money market: Correlations and clustering on the e-MID trading platform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6528-6542.
    15. Maslov, Sergei & Sneppen, Kim & Zaliznyak, Alexei, 2004. "Detection of topological patterns in complex networks: correlation profile of the internet," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 529-540.
    16. D. Garlaschelli & M. I. Loffredo, 2004. "Fitness-dependent topological properties of the World Trade Web," Papers cond-mat/0403051, arXiv.org, revised Oct 2004.
    17. Tiziano Squartini & Iman van Lelyveld & Diego Garlaschelli, 2013. "Early-warning signals of topological collapse in interbank networks," Papers 1302.2063, arXiv.org, revised Nov 2013.
    18. Fricke, Daniel & Finger, Karl & Lux, Thomas, 2013. "On assortative and disassortative mixing in scale-free networks: The case of interbank credit networks," Kiel Working Papers 1830, Kiel Institute for the World Economy (IfW Kiel).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bartesaghi, Paolo & Clemente, Gian Paolo & Grassi, Rosanna & Luu, Duc Thi, 2022. "The multilayer architecture of the global input-output network and its properties," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 304-341.
    2. Tiziano Squartini & Guido Caldarelli & Giulio Cimini & Andrea Gabrielli & Diego Garlaschelli, 2018. "Reconstruction methods for networks: the case of economic and financial systems," Papers 1806.06941, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Assaf Almog & Rhys Bird & Diego Garlaschelli, 2015. "Enhanced Gravity Model of trade: reconciling macroeconomic and network models," Papers 1506.00348, arXiv.org, revised Feb 2019.
    3. Marco Dueñas & Giorgio Fagiolo, 2013. "Modeling the International-Trade Network: a gravity approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 155-178, April.
    4. Marco Dueñas & Giorgio Fagiolo, 2014. "Global Trade Imbalances: A Network Approach," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 17(03n04), pages 1-29.
    5. Hoppe, K. & Rodgers, G.J., 2015. "A microscopic study of the fitness-dependent topology of the world trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 64-74.
    6. L. Bargigli & G. di Iasio & L. Infante & F. Lillo & F. Pierobon, 2015. "The multiplex structure of interbank networks," Quantitative Finance, Taylor & Francis Journals, vol. 15(4), pages 673-691, April.
    7. Iori, Giulia & Mantegna, Rosario N. & Marotta, Luca & Miccichè, Salvatore & Porter, James & Tumminello, Michele, 2015. "Networked relationships in the e-MID interbank market: A trading model with memory," Journal of Economic Dynamics and Control, Elsevier, vol. 50(C), pages 98-116.
    8. Mungo, Luca & Lafond, François & Astudillo-Estévez, Pablo & Farmer, J. Doyne, 2023. "Reconstructing production networks using machine learning," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    9. Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2013. "Null models of economic networks: the case of the world trade web," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 75-107, April.
    10. 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.
    11. Marco Bardoscia & Paolo Barucca & Stefano Battiston & Fabio Caccioli & Giulio Cimini & Diego Garlaschelli & Fabio Saracco & Tiziano Squartini & Guido Caldarelli, 2021. "The Physics of Financial Networks," Papers 2103.05623, arXiv.org.
    12. Sadamori Kojaku & Giulio Cimini & Guido Caldarelli & Naoki Masuda, 2018. "Structural changes in the interbank market across the financial crisis from multiple core-periphery analysis," Papers 1802.05139, arXiv.org.
    13. Piero Mazzarisi & Paolo Barucca & Fabrizio Lillo & Daniele Tantari, 2017. "A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market," Papers 1801.00185, arXiv.org.
    14. Vandermarliere, Benjamin & Karas, Alexei & Ryckebusch, Jan & Schoors, Koen, 2015. "Beyond the power law: Uncovering stylized facts in interbank networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 443-457.
    15. 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.
    16. 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.
    17. Temizsoy, Asena & Iori, Giulia & Montes-Rojas, Gabriel, 2017. "Network centrality and funding rates in the e-MID interbank market," Journal of Financial Stability, Elsevier, vol. 33(C), pages 346-365.
    18. Adão, Luiz F.S. & Silveira, Douglas & Ely, Regis A. & Cajueiro, Daniel O., 2022. "The impacts of interest rates on banks’ loan portfolio risk-taking," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    19. Vittorio Carlei & Francesca Affortunato & Alessandro Marra & Marco Brogi, 2019. "Does centrality of importing countries affect export prices in the global trade?," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(1), pages 529-551, January.
    20. Tiziano Squartini & Guido Caldarelli & Giulio Cimini & Andrea Gabrielli & Diego Garlaschelli, 2018. "Reconstruction methods for networks: the case of economic and financial systems," Papers 1806.06941, arXiv.org.

    More about this item

    Keywords

    Interbank Network; Structural Correlations; Clustering Coefficients; Configuration Models; Network Reconstruction;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G01 - Financial Economics - - General - - - Financial Crises
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:cauewp:201702. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/vakiede.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.