Arakelian, V;
Dellaportas, P;
Savona, R;
Vezzoli, M;
(2019)
Sovereign risk zones in Europe during and after the debt crisis.
Quantitative Finance
, 19
(6)
pp. 961-980.
10.1080/14697688.2018.1562197.
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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 basis points in the period 2013–2017 due to contagion mitigation.
Type: | Article |
---|---|
Title: | Sovereign risk zones in Europe during and after the debt crisis |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1080/14697688.2018.1562197 |
Publisher version: | https://doi.org/10.1080/14697688.2018.1562197 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | contagion, copula, credit default swaps, machine learning, regression trees, systemic risk |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10073074 |
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