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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. 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.
    3. 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).
    4. 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).
    5. 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).
    6. 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).
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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).
    12. Fabrizio Lillo & Giorgio Rizzini, 2024. "Modelling shock propagation and resilience in financial temporal networks," Papers 2407.09340, arXiv.org.
    13. 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).
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.

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