IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v10y2022i3p54-d861100.html
   My bibliography  Save this article

A Deep Learning Approach to Dynamic Interbank Network Link Prediction

Author

Listed:
  • Haici Zhang

    (The Institute for Financial Services Analytics, University of Delaware, Newark, DE 19716, USA)

Abstract

Lehman Brothers’ failure in 2008 demonstrated the importance of understanding interconnectedness in interbank networks. The interbank market plays a significant role in facilitating market liquidity and providing short-term funding for each other to smooth liquidity shortages. Knowing the trading relationship could also help understand risk contagion among banks. Therefore, future lending relationship prediction is important to understand the dynamic evolution of interbank networks. To achieve the goal, we apply a deep learning framework model of interbank lending to an electronic trading interbank network for temporal trading relationship prediction. There are two important components of the model, which are the Graph convolutional network (GCN) and the Long short-term memory (LSTM) model. The GCN and LSTM components together capture the spatial–temporal information of the dynamic network snapshots. Compared with the Discrete autoregressive model and Dynamic latent space model, our proposed model achieves better performance in both the precrisis and the crisis period.

Suggested Citation

  • Haici Zhang, 2022. "A Deep Learning Approach to Dynamic Interbank Network Link Prediction," IJFS, MDPI, vol. 10(3), pages 1-16, July.
    • Handle: RePEc:gam:jijfss:v:10:y:2022:i:3:p:54-:d:861100
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/10/3/54/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7072/10/3/54/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Upper, Christian, 2011. "Simulation methods to assess the danger of contagion in interbank markets," Journal of Financial Stability, Elsevier, vol. 7(3), pages 111-125, August.
    2. 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.
    3. Leventides, John & Loukaki, Kalliopi & Papavassiliou, Vassilios G., 2019. "Simulating financial contagion dynamics in random interbank networks," Journal of Economic Behavior & Organization, Elsevier, vol. 158(C), pages 500-525.
    4. Linardi, Fernando & Diks, Cees & van der Leij, Marco & Lazier, Iuri, 2020. "Dynamic interbank network analysis using latent space models," Journal of Economic Dynamics and Control, Elsevier, vol. 112(C).
    5. Bräuning, Falk & Koopman, Siem Jan, 2020. "The dynamic factor network model with an application to international trade," Journal of Econometrics, Elsevier, vol. 216(2), pages 494-515.
    6. Daniel K. Sewell & Yuguo Chen, 2015. "Latent Space Models for Dynamic Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1646-1657, December.
    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. Shu Takahashi & Kento Yamamoto & Shumpei Kobayashi & Ryoma Kondo & Ryohei Hisano, 2024. "Dynamic Link and Flow Prediction in Bank Transfer Networks," Papers 2409.08718, arXiv.org, revised Oct 2024.

    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. Linardi, Fernando & Diks, Cees & van der Leij, Marco & Lazier, Iuri, 2020. "Dynamic interbank network analysis using latent space models," Journal of Economic Dynamics and Control, Elsevier, vol. 112(C).
    2. 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.
    3. Valentina Macchiati & Giuseppe Brandi & Tiziana Di Matteo & Daniela Paolotti & Guido Caldarelli & Giulio Cimini, 2022. "Systemic liquidity contagion in the European interbank market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(2), pages 443-474, April.
    4. Siklos, Pierre L. & Stefan, Martin, 2021. "Exchange rate shocks in multicurrency interbank markets," Journal of Financial Stability, Elsevier, vol. 55(C).
    5. Kartik Anand & Ben Craig & Goetz von Peter, 2015. "Filling in the blanks: network structure and interbank contagion," Quantitative Finance, Taylor & Francis Journals, vol. 15(4), pages 625-636, April.
    6. Batiz-Zuk, Enrique & López-Gallo, Fabrizio & Martínez-Jaramillo, Serafín & Solórzano-Margain, Juan Pablo, 2016. "Calibrating limits for large interbank exposures from a system-wide perspective," Journal of Financial Stability, Elsevier, vol. 27(C), pages 198-216.
    7. 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.
    8. Kanno, Masayasu, 2016. "The network structure and systemic risk in the global non-life insurance market," Insurance: Mathematics and Economics, Elsevier, vol. 67(C), pages 38-53.
    9. Montagna, Mattia & Lux, Thomas, 2013. "Hubs and resilience: Towards more realistic models of the interbank markets," Kiel Working Papers 1826, Kiel Institute for the World Economy (IfW Kiel).
    10. in 't Veld, Daan & van der Leij, Marco & Hommes, Cars, 2020. "The formation of a core-periphery structure in heterogeneous financial networks," Journal of Economic Dynamics and Control, Elsevier, vol. 119(C).
    11. Sümer, Tuba Pelin & Özyıldırım, Süheyla, 2019. "Do banking groups shape the network structure? Evidence from Turkish interbank market," International Review of Financial Analysis, Elsevier, vol. 66(C).
    12. Paolo Bartesaghi & Michele Benzi & Gian Paolo Clemente & Rosanna Grassi & Ernesto Estrada, 2019. "Risk-dependent centrality in economic and financial networks," Papers 1907.07908, arXiv.org, revised Apr 2020.
    13. Tovar-García, Edgar Demetrio, 2016. "Who can better monitor a bank than another bank? Mechanisms of discipline in the Mexican interbank market ||¿Quién mejor que un banco para monitorear otro banco? Mecanismos de disciplina en el mercado," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 21(1), pages 205-229, June.
    14. Lee, Seung Hwan, 2013. "Systemic liquidity shortages and interbank network structures," Journal of Financial Stability, Elsevier, vol. 9(1), pages 1-12.
    15. Teteryatnikova, Mariya, 2014. "Systemic risk in banking networks: Advantages of “tiered” banking systems," Journal of Economic Dynamics and Control, Elsevier, vol. 47(C), pages 186-210.
    16. Liu, Anqi & Paddrik, Mark & Yang, Steve Y. & Zhang, Xingjia, 2020. "Interbank contagion: An agent-based model approach to endogenously formed networks," Journal of Banking & Finance, Elsevier, vol. 112(C).
    17. Lopomo Beteto Wegner, Danilo, 2020. "Liquidity policies and financial fragility," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 135-153.
    18. Domenico Di Gangi & Giacomo Bormetti & Fabrizio Lillo, 2022. "Score Driven Generalized Fitness Model for Sparse and Weighted Temporal Networks," Papers 2202.09854, arXiv.org, revised Mar 2022.
    19. Aldasoro, Iñaki & Hüser, Anne-Caroline & Kok, Christoffer, 2020. "Contagion accounting," Bank of England working papers 897, Bank of England.
    20. Agur, Itai, 2014. "Bank risk within and across equilibria," Journal of Banking & Finance, Elsevier, vol. 48(C), pages 322-333.

    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:gam:jijfss:v:10:y:2022:i:3:p:54-:d:861100. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.