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Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction

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  • Di Gangi, Domenico
  • Lillo, Fabrizio
  • Pirino, Davide

Abstract

Monitoring and assessing systemic risk in financial markets is of great importance but it often requires data that are unavailable or available at a very low frequency. For this reason, systemic risk assessment with partial information is potentially very useful for regulators and other stakeholders. In this paper we consider systemic risk due to fire sales spillovers and portfolio rebalancing by using the risk metrics defined by Greenwood et al. (2015). By using a method based on the constrained minimization of the Cross Entropy, we show that it is possible to assess aggregated and single bank’s systemicness and vulnerability, using only the information on the size of each bank and the capitalization of each investment asset. We also compare our approach with an alternative widespread application of the Maximum Entropy principle allowing to derive graph probability distributions and generating scenarios and we use it to propose a statistical test for a change in banks’ vulnerability to systemic events.

Suggested Citation

  • Di Gangi, Domenico & Lillo, Fabrizio & Pirino, Davide, 2018. "Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction," Journal of Economic Dynamics and Control, Elsevier, vol. 94(C), pages 117-141.
  • Handle: RePEc:eee:dyncon:v:94:y:2018:i:c:p:117-141
    DOI: 10.1016/j.jedc.2018.07.001
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    Cited by:

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    3. Ramadiah, Amanah & Caccioli, Fabio & Fricke, Daniel, 2019. "Reconstructing and stress testing credit networks," LSE Research Online Documents on Economics 118938, London School of Economics and Political Science, LSE Library.
    4. Ramadiah, Amanah & Caccioli, Fabio & Fricke, Daniel, 2020. "Reconstructing and stress testing credit networks," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    5. Pang, Raymond Ka-Kay & Veraart, Luitgard A. M., 2023. "Assessing and mitigating fire sales risk under partial information," LSE Research Online Documents on Economics 120171, London School of Economics and Political Science, LSE Library.
    6. Carolina Becatti & Guido Caldarelli & Renaud Lambiotte & Fabio Saracco, 2019. "Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-16, December.
    7. Andrea Bacilieri & Pablo Austudillo-Estevez, 2023. "Reconstructing firm-level input-output networks from partial information," Papers 2304.00081, arXiv.org.
    8. Chao, Wang & Jing, Ma & Xiaoxing, Liu, 2023. "Optimizing systemic risk through credit network reconstruction," Emerging Markets Review, Elsevier, vol. 57(C).
    9. Mazzarisi, Piero & Lillo, Fabrizio & Marmi, Stefano, 2019. "When panic makes you blind: A chaotic route to systemic risk," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 176-199.
    10. Lillo, Fabrizio & Livieri, Giulia & Marmi, Stefano & Solomko, Anton & Vaienti, Sandro, 2023. "Analysis of bank leverage via dynamical systems and deep neural networks," LSE Research Online Documents on Economics 119917, London School of Economics and Political Science, LSE Library.
    11. Roy Cerqueti & Gian Paolo Clemente & Rosanna Grassi, 2018. "Systemic risk assessment through high order clustering coefficient," Papers 1810.13250, arXiv.org, revised Jul 2020.
    12. Ramadiah, Amanah & Fricke, Daniel & Caccioli, Fabio, 2022. "Backtesting macroprudential stress tests," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
    13. Wu, Shan & Tong, Mu & Yang, Zhongyi & Zhang, Tianyi, 2021. "Interconnectedness, systemic risk, and the influencing factors: Some evidence from China’s financial institutions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    14. James Paulin & Anisoara Calinescu & Michael Wooldridge, 2018. "Understanding Flash Crash Contagion and Systemic Risk: A Micro-Macro Agent-Based Approach," Papers 1805.08454, arXiv.org.
    15. Andreas Muhlbacher & Thomas Guhr, 2018. "Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations," Papers 1803.00261, arXiv.org.
    16. Michel Alexandre & Thiago Christiano Silva & Colm Connaughton & Francisco A. Rodrigues, 2021. "The Role of (non-)Topological Features as Drivers of Systemic Risk: a machine learning approach," Working Papers Series 556, Central Bank of Brazil, Research Department.
    17. Alexandre, Michel & Silva, Thiago Christiano & Connaughton, Colm & Rodrigues, Francisco A., 2021. "The drivers of systemic risk in financial networks: a data-driven machine learning analysis," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    18. Barucca, Paolo & Mahmood, Tahir & Silvestri, Laura, 2021. "Common asset holdings and systemic vulnerability across multiple types of financial institution," Journal of Financial Stability, Elsevier, vol. 52(C).
    19. 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.
    20. Andreas Mühlbacher & Thomas Guhr, 2018. "Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations," Risks, MDPI, vol. 6(2), pages 1-25, April.
    21. Paulin, James & Calinescu, Anisoara & Wooldridge, Michael, 2019. "Understanding flash crash contagion and systemic risk: A micro–macro agent-based approach," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 200-229.
    22. Matteo Bruno & Dario Mazzilli & Aurelio Patelli & Tiziano Squartini & Fabio Saracco, 2023. "Inferring comparative advantage via entropy maximization," Papers 2304.12245, arXiv.org.
    23. Roy Cerqueti & Gian Paolo Clemente & Rosanna Grassi, 2021. "Systemic risk assessment through high order clustering coefficient," Annals of Operations Research, Springer, vol. 299(1), pages 1165-1187, April.
    24. Pang, Raymond Ka-Kay & Veraart, Luitgard Anna Maria, 2023. "Assessing and mitigating fire sales risk under partial information," Journal of Banking & Finance, Elsevier, vol. 155(C).
    25. Fabrizio Lillo & Giulia Livieri & Stefano Marmi & Anton Solomko & Sandro Vaienti, 2021. "Analysis of bank leverage via dynamical systems and deep neural networks," Papers 2104.04960, arXiv.org.

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    More about this item

    Keywords

    Systemic risk; Maximum entropy; Fire sales; Financial networks; Liquidity;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • G01 - Financial Economics - - General - - - Financial Crises
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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