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Estimating the impact of supply chain network contagion on financial stability

Author

Listed:
  • Zlata Tabachov'a
  • Christian Diem
  • Andr'as Borsos
  • Csaba Burger
  • Stefan Thurner

Abstract

Realistic credit risk assessment, the estimation of losses from counterparty's failure, is central for the financial stability. Credit risk models focus on the financial conditions of borrowers and only marginally consider other risks from the real economy, supply chains in particular. Recent pandemics, geopolitical instabilities, and natural disasters demonstrated that supply chain shocks do contribute to large financial losses. Based on a unique nation-wide micro-dataset, containing practically all supply chain relations of all Hungarian firms, together with their bank loans, we estimate how firm-failures affect the supply chain network, leading to potentially additional firm defaults and additional financial losses. Within a multi-layer network framework we define a financial systemic risk index (FSRI) for every firm, quantifying these expected financial losses caused by its own- and all the secondary defaulting loans caused by supply chain network (SCN) shock propagation. We find a small fraction of firms carrying substantial financial systemic risk, affecting up to 16% of the banking system's overall equity. These losses are predominantly caused by SCN contagion. For every bank we calculate the expected loss (EL), value at risk (VaR) and expected shortfall (ES), with and without accounting for SCN contagion. We find that SCN contagion amplifies the EL, VaR, and ES by a factor of 4.3, 4.5, and 3.2, respectively. These findings indicate that for a more complete picture of financial stability and realistic credit risk assessment, SCN contagion needs to be considered. This newly quantified contagion channel is of potential relevance for regulators' future systemic risk assessments.

Suggested Citation

  • Zlata Tabachov'a & Christian Diem & Andr'as Borsos & Csaba Burger & Stefan Thurner, 2023. "Estimating the impact of supply chain network contagion on financial stability," Papers 2305.04865, arXiv.org.
    • Handle: RePEc:arx:papers:2305.04865
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    References listed on IDEAS

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    1. Peydró, José-Luis & Jiménez, Gabriel & Kenan, Huremovic & Moral-Benito, Enrique & Vega-Redondo, Fernando, 2020. "Production and financial networks in interplay: Crisis evidence from supplier-customer and credit registers," CEPR Discussion Papers 15277, C.E.P.R. Discussion Papers.
    2. Acharya, Viral & Engle, Robert & Pierret, Diane, 2014. "Testing macroprudential stress tests: The risk of regulatory risk weights," Journal of Monetary Economics, Elsevier, vol. 65(C), pages 36-53.
    3. Christian Diem & Andr'as Borsos & Tobias Reisch & J'anos Kert'esz & Stefan Thurner, 2023. "Estimating the loss of economic predictability from aggregating firm-level production networks," Papers 2302.11451, arXiv.org.
    4. Dror Y. Kenett & Sary Levy-Carciente & Adam Avakian & H. Eugene Stanley & Shlomo Havlin, 2015. "Dynamical Macroprudential Stress Testing Using Network Theory," Working Papers 15-12, Office of Financial Research, US Department of the Treasury.
    5. Levy-Carciente, Sary & Kenett, Dror Y. & Avakian, Adam & Stanley, H. Eugene & Havlin, Shlomo, 2015. "Dynamical macroprudential stress testing using network theory," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 164-181.
    6. Andras Borsos & Bence Mero, 2020. "Shock Propagation in the Banking System with Real Economy Feedback," MNB Working Papers 2020/6, Magyar Nemzeti Bank (Central Bank of Hungary).
    7. Andras Borsos & Martin Stancsics, 2020. "Unfolding the hidden structure of the Hungarian multi-layer firm network," MNB Occasional Papers 2020/139, Magyar Nemzeti Bank (Central Bank of Hungary).
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