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A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market

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  • Mazzarisi, P.
  • Barucca, P.
  • Lillo, F.
  • Tantari, D.

Abstract

We propose a dynamic network model where two mechanisms control the probability of a link between two nodes: (i) the existence or absence of this link in the past, and (ii) node-specific latent variables (dynamic fitnesses) describing the propensity of each node to create links. Assuming a Markov dynamics for both mechanisms, we propose an Expectation-Maximization algorithm for model estimation and inference of the latent variables. The estimated parameters and fitnesses can be used to forecast the presence of a link in the future. We apply our methodology to the e-MID interbank network for which the two linkage mechanisms are associated with two different trading behaviors in the process of network formation, namely preferential trading and trading driven by node-specific characteristics. The empirical results allow to recognize preferential lending in the interbank market and indicate how a method that does not account for time-varying network topologies tends to overestimate preferential linkage.

Suggested Citation

  • Mazzarisi, P. & Barucca, P. & Lillo, F. & Tantari, D., 2020. "A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market," European Journal of Operational Research, Elsevier, vol. 281(1), pages 50-65.
    • Handle: RePEc:eee:ejores:v:281:y:2020:i:1:p:50-65
    DOI: 10.1016/j.ejor.2019.07.024
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    Citations

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    Cited by:

    1. Saroyan, Susanna, 2024. "Counterparty choice, maturity shifts and market freezes: Lessons from the European interbank market," Journal of Economic Dynamics and Control, Elsevier, vol. 160(C).
    2. Hüser, Anne-Caroline & Lepore, Caterina & Veraart, Luitgard Anna Maria, 2024. "How does the repo market behave under stress? Evidence from the COVID-19 crisis," Journal of Financial Stability, Elsevier, vol. 70(C).
    3. Seabrook, Isobel E. & Barucca, Paolo & Caccioli, Fabio, 2021. "Evaluating structural edge importance in temporal networks," LSE Research Online Documents on Economics 112515, London School of Economics and Political Science, LSE Library.
    4. 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.
    5. León, Carlos & Miguélez, Javier, 2021. "Interbank relationship lending revisited: Are the funds available at a similar price?," Research in International Business and Finance, Elsevier, vol. 58(C).
    6. Piero Mazzarisi & Silvia Zaoli & Carlo Campajola & Fabrizio Lillo, 2020. "Tail Granger causalities and where to find them: extreme risk spillovers vs. spurious linkages," Papers 2005.01160, arXiv.org, revised May 2021.
    7. Jung, Hohyun, 2023. "Eliminating the biases of user influence and item popularity in bipartite networks: A case study of Flickr and Netflix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    8. Chen, Wei & Qu, Shuai & Jiang, Manrui & Jiang, Cheng, 2021. "The construction of multilayer stock network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    9. Rabbani, Fereshteh & Khraisha, Tamer & Abbasi, Fatemeh & Jafari, Gholam Reza, 2021. "Memory effects on link formation in temporal networks: A fractional calculus approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    10. Deborah Noguera & Gabriel Montes-Rojas, 2023. "Minskyan model with credit rationing in a network economy," SN Business & Economics, Springer, vol. 3(3), pages 1-26, March.
    11. Mazzarisi, Piero & Zaoli, Silvia & Campajola, Carlo & Lillo, Fabrizio, 2020. "Tail Granger causalities and where to find them: Extreme risk spillovers vs spurious linkages," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    12. Hüser, Anne-Caroline & Lepore, Caterina & Veraart, Luitgard A. M., 2024. "How does the repo market behave under stress? Evidence from the COVID-19 crisis," LSE Research Online Documents on Economics 121347, London School of Economics and Political Science, LSE Library.
    13. Oliver E. Williams & Lucas Lacasa & Ana P. Millán & Vito Latora, 2022. "The shape of memory in temporal networks," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    14. Gabriele Tedeschi & Fabio Caccioli & Maria Cristina Recchioni, 2020. "Taming financial systemic risk: models, instruments and early warning indicators," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 1-7, January.
    15. Cheng, Qixiu & Lin, Yuqian & Zhou, Xuesong (Simon) & Liu, Zhiyuan, 2024. "Analytical formulation for explaining the variations in traffic states: A fundamental diagram modeling perspective with stochastic parameters," European Journal of Operational Research, Elsevier, vol. 312(1), pages 182-197.
    16. Chang, Miaoxin & Huang, Xianzhen & Coolen, Frank PA & Coolen-Maturi, Tahani, 2023. "New reliability model for complex systems based on stochastic processes and survival signature," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1349-1364.
    17. Carlo Campajola & Domenico Di Gangi & Fabrizio Lillo & Daniele Tantari, 2020. "Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model," Papers 2007.15545, arXiv.org, revised Aug 2021.
    18. Deborah Noguera & Gabriel Montes-Rojas, 2022. "Credit-constrained fluctuations and uncertainty in a network economy," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(80), pages 5-52, November.
    19. Fabrizio Lillo & Giorgio Rizzini, 2024. "Modelling shock propagation and resilience in financial temporal networks," Papers 2407.09340, arXiv.org.

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