Skip to main content

Measuring bank contagion in Europe using binary spatial regression models

Abstract

The recent European sovereign debt crisis clearly illustrates the importance of measuring the contagion effects of bank failures. Indeed, to better understand and monitor contagion risk, the European Central Bank has assumed the supervision of the largest banks in each of the member states. We propose a measure of contagion risk based on the spatial autocorrelation parameter of a binary spatial autoregressive model. Using different specifications of the interbank connectivity matrix, we estimate the contagion parameter for banks within the Eurozone, between 1996 and 2012. We provide evidence of high levels of systemic risk due to contagion during the European sovereign debt crisis.

This is a preview of subscription content, access via your institution.

Notes

  1. 1.

    See http://www.bis.org.

References

  1. Acemoglu D, Ozdaglar A and Tahbaz-Salehi A (2015). Systemic risk and stability in financial networks. American Economic Review 105(2):564–608.

    Article  Google Scholar 

  2. Allen F and Babus A (2008) Networks in Finance. Technical report. Wharton Financial Institutions Center Working paper.

  3. Altman EI (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance 23(4):589–609.

    Article  Google Scholar 

  4. Anselin L (2002). Under the hood. Issues in the specification and interpretation of spatial regression models. Agricultural Economics 27(2002):247–267.

    Article  Google Scholar 

  5. Arakelian V and Dellaportas P (2010). Contagion determination via copula and volatility threshold models. Quantitative Finance 12(2):295–310.

    Article  Google Scholar 

  6. Arena M (2008). Bank failures and bank fundamentals: A comparative analysis of Latin America and East Asia during the nineties using bank-level data. Journal of Banking & Finance 32(2):299–310.

    Article  Google Scholar 

  7. Beron KJ, Wim PM and Vijverberg (2004). Probit in a spatial context: A Monte Carlo analysis. In Anselin L, Florax RJGM and Rey SJ (eds).Advances in Spatial Econometrics Methodology Tools and Applications pp. 169–195. Springer: Berlin.

    Chapter  Google Scholar 

  8. Billio M, Getmansky M, Lo AW and Pelizzon L (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics 104(3):535–559.

    Article  Google Scholar 

  9. Bongini P, Claessens S and Ferri G. (2001). The political economy of distress in East Asian financial institutions. Journal of Financial Services Research 19(1):5–25.

    Article  Google Scholar 

  10. Boss M, Elsinger H, Thurner S and Summer M (2004). Network topology of the interbank market. Quantitative Finance 4(6):677–684.

    Article  Google Scholar 

  11. Brown CO and Serdar Dinc I (2011). Too many to fail? Evidence of regulatory forbearance when the banking sector is weak. Review of Financial Studies 24(4):1378–1405.

    Article  Google Scholar 

  12. Buehler K, Samandari H and Mazingo C (2009). Capital Ratios and Financial Distress: Lessons from the Crisis. Technical report working paper.

  13. Calabrese R and Elkink JA (2014). Estimators of binary spatial autoregressive models: A Monte Carlo study. Journal of Regional Science 54(4):664–687.

    Article  Google Scholar 

  14. Calabrese R and Elkink JA (2016). Estimating binary spatial autoregressive models for rare events. Advances in Econometrics 37(2016):147–168.

    Google Scholar 

  15. Calabrese R and Giudici P (2015). Estimating bank default with generalised extreme value models. Journal of the Operational Research Society 66(11):1783-1792.

    Article  Google Scholar 

  16. Calabrese R and Osmetti S (2014). Modelling cross-border systemic risk in the European banking sector: A copula approach. arXiv:1411.1348.

  17. Cerchiello P and Giudici P (2016). Conditional graphical models for systemic risk estimation. Expert systems with applications 43(C): 165-174.

    Article  Google Scholar 

  18. Cerchiello P, Giudici P and Nicola G (2017). Twitter data models for bank risk contagion. Neurocomputing (to appear)

  19. Chesher A and Irish M (1987). Residual analysis in the grouped and censored normal linear model. Journal of Econometrics 34(1987):33–61.

    Article  Google Scholar 

  20. Cole RA and Gunther JW (1998). Predicting bank failures: A comparison of on-and off-site monitoring systems. Journal of Financial Services Research 13(2):103–117.

    Article  Google Scholar 

  21. Cox DR and Snell EJ (1968). A general definition of residuals. Journal of the Royal Statistical Society B 30(2):248–275.

    Google Scholar 

  22. Davis, EP and Karim D (2008a). Comparing early warning systems for banking crises. Journal of Financial stability 4(2):89–120.

    Article  Google Scholar 

  23. Davis EP and Karim D (2008b). Comparing early warning systems for banking crises. Journal of Financial stability 4(2):89–120.

    Article  Google Scholar 

  24. De Lisa R, Zedda S, Vallascas F, Campolongo F and Marchesi M (2011). Modelling deposit insurance scheme losses in a Basel 2 Framework. Journal of Financial Services Research 40(3): 123–141.

    Article  Google Scholar 

  25. Elliot M, Golub B and Jackson MO (2014). Financial networks and contagion. American Economic Review 104(10):3115–53.

    Article  Google Scholar 

  26. Fleming MM (2004). Techniques for estimating spatially dependent discrete choice models. In: Anselin L, Florax RJGM and Rey SJ (eds) Advances in spatial econometrics Methodology tools and applications. Berlin, Springer, pp. 145–167.

    Chapter  Google Scholar 

  27. Franzese RJ and Hays JC (2008). Interdependence in comparative politics substance, theory, empirics, substance. Comparative Political Studies 41(4–5):742–780.

    Article  Google Scholar 

  28. Gai P, Haldane A and Kapadia S (2011). Complexity, concentration and contagion. Journal of Monetary Economics 58(5):453–470.

    Article  Google Scholar 

  29. Giudici P and Spelta A (2016). Graphical network models for international financial flows. Journal of Business and Economic Statistics 34(1):126–138.

    Article  Google Scholar 

  30. González-Hermosillo B (1999). Determinants of Ex-Ante Banking System Distress: A Macro-Micro Empirical Exploration of Some Recent Episodes (EPub). International Monetary Fund.

  31. Gropp R, Lo Duca M and Vesala J (2009). Cross-border contagion risk in Europe. Journal of Central Banking 5(1):367–381.

    Google Scholar 

  32. Halaj G (2013). Optimal Asset Structure of a Bank: Bank Reactions to Stressful Market Conditions. Technical report European Central Bank. Working paper 1533.

  33. Hałaj G and Kok C (2013). Assessing Interbank Contagion Using Simulated Networks. European Central Bank.

  34. Kanno M (2012). Default forecasting considering correlation between business and credit cycles. Journal of Applied Finance & Banking 2(5):275–305.

    Google Scholar 

  35. Kenny G, Kostka T and Masera F (2013). CanMacroeconomists Forecast Risk? Event-Based Evidence from the EuroArea SPF. Technical report European Central Bank.

  36. Klier T and McMillen DP (2008). Clustering of auto supplier plants in the United States: Generalized method of moments spatial logit for large samples. Journal of Business & Economic Statistics 26(4):460–471.

    Article  Google Scholar 

  37. Klomp J and De Haan J. (2012). Banking risk and regulation: Does one size fit all? Journal of Banking & Finance 36(12):3197–3212.

    Article  Google Scholar 

  38. Koopman SJ, Lucas A and Schwaab B (2012). Dynamic factor models with macro, frailty, and industry effects for US default counts: The credit crisis of 2008. Journal of Business & Economic Statistics 30(4):521–532.

    Article  Google Scholar 

  39. Krause A and Giansante S (2012). Interbank lending and the spread of bank failures: A network model of systemic risk. Journal of Economic Behavior and Organization 83(3):583–608.

    Article  Google Scholar 

  40. LeSage J and Pace RK. 2009. Introduction to Spatial Econometrics. Chapman and Hall/CRC, Boca Raton

    Book  Google Scholar 

  41. Merton RC (1974). On the pricing of corporate debt: The risk structure of interest rates*. The Journal of Finance 29(2):449–470.

    Google Scholar 

  42. Minoiu C, Kang C, Subrahmanian VS and Berea A (2013). Does Financial Connectedness Predict Crises? Technical report IFM working paper.

  43. Mistrulli PE (2011). Assessing financial contagion in the interbank market: Maximum entropy versus observed interbank lending patterns. Journal of Banking & Finance 35(5):1114–1127.

    Article  Google Scholar 

  44. Pinkse J and Slade ME (1998). Contracting in space: an application of spatial statistics to discrete-choice models. Journal of Econometrics 85:125–154.

    Article  Google Scholar 

  45. Sinkey JF (1975). A multivariate statistical analysis of the characteristics of problem banks. The Journal of Finance 30(1):21–36.

    Article  Google Scholar 

  46. Squartini T, van Lelyveld I and Garlaschelli D (2013). Early-warning signals of topological collapse in interbank networks. Scientific Reports 3, 3357. doi:10.1038/srep03357.

  47. Tam KY and Kiang MY (1992). Managerial applications of neural networks: The case of bank failure predictions. Management science 38(7):926–947.

    Article  Google Scholar 

  48. Upper C and Worms AE (2004). Estimating bilateral exposures in the German interbank market: Is there a danger of contagion? European Economic Review 48(4):827–849.

    Article  Google Scholar 

  49. Upper C (2011). Simulation methods to assess the danger of contagion in interbank markets. Journal of Financial Stability 7(2011):111–125.

    Article  Google Scholar 

  50. Vasicek OA (1984). Credit valuation.

  51. Vázquez FF and Federico P (2012). Bank funding structures and risk: Evidence from the global financial crisis. International Monetary Fund.

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Paolo S. Giudici.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Calabrese, R., Elkink, J.A. & Giudici, P.S. Measuring bank contagion in Europe using binary spatial regression models. J Oper Res Soc 68, 1503–1511 (2017). https://doi.org/10.1057/s41274-017-0189-4

Download citation

Keywords

  • contagion risk
  • spatial autoregressive models
  • European banks