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Sovereign risk zones in Europe during and after the debt crisis

Author

Listed:
  • Veni Arakelian
  • Petros Dellaportas
  • Roberto Savona
  • Marika Vezzoli

Abstract

We employ a machine learning approach to build a European sovereign risk stratification using macroeconomic fundamentals and contagion measures, proxied by copula-based credit default swap (CDS) dependencies over the period 2008–2017, for France, Germany, Greece, Ireland, Italy, Portugal, and Spain. By adopting a recursive partitioning strategy, we detect specific risk zones varying from safe to high risk based on key predictors, and we construct their specification by assigning specific risk thresholds. While key macroeconomic fundamentals such as Debt/GDP and the unemployment rate remained the same and maintained the same risk thresholds during the sub-periods 2008–2013 and 2013–2017, the CDS spreads contagion dropped significantly over the post-Quantitative Easing years, lowering the corresponding risk thresholds. We estimate an impact on CDS spreads approximately of $ -105 $ −105 basis points in the period 2013–2017 due to contagion mitigation.

Suggested Citation

  • Veni Arakelian & Petros Dellaportas & Roberto Savona & Marika Vezzoli, 2019. "Sovereign risk zones in Europe during and after the debt crisis," Quantitative Finance, Taylor & Francis Journals, vol. 19(6), pages 961-980, June.
    • Handle: RePEc:taf:quantf:v:19:y:2019:i:6:p:961-980
    DOI: 10.1080/14697688.2018.1562197
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    Citations

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    Cited by:

    1. Foglia, Matteo & Angelini, Eliana, 2020. "The diabolical sovereigns/banks risk loop: A VAR quantile design," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    2. Guillaume Belly & Lukas Boeckelmann & Carlos Mateo Caicedo Graciano & Alberto Di Iorio & Klodiana Istrefi & Vasileios Siakoulis & Arthur Stalla‐Bourdillon, 2023. "Forecasting sovereign risk in the Euro area via machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 657-684, April.
    3. Anastasios Petropoulos & Vasilis Siakoulis & Evangelos Stavroulakis, 2022. "Towards an early warning system for sovereign defaults leveraging on machine learning methodologies," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(2), pages 118-129, April.
    4. Moreno Badia, Marialuz & Medas, Paulo & Gupta, Pranav & Xiang, Yuan, 2022. "Debt is not free," Journal of International Money and Finance, Elsevier, vol. 127(C).
    5. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    6. Gilles Dufrénot & Fredj Jawadi & Zied Ftiti, 2022. "Sovereign bond market integration in the euro area: a new empirical conceptualization," Annals of Operations Research, Springer, vol. 318(1), pages 147-161, November.
    7. Bitetto, Alessandro & Cerchiello, Paola & Mertzanis, Charilaos, 2023. "Measuring financial soundness around the world: A machine learning approach," International Review of Financial Analysis, Elsevier, vol. 85(C).

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