Spillovers across European sovereign credit markets and role of surprise and uncertainty
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DOI: 10.1080/00036846.2019.1659930
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Cited by:
- Bo Gao, 2022. "The Use of Machine Learning Combined with Data Mining Technology in Financial Risk Prevention," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1385-1405, April.
- Marta Gómez-Puig & Mary Pieterse-Bloem & Simón Sosvilla-Rivero, 2022. ""Dynamic connectedness between credit and liquidity risks in EMU sovereign debt markets"," IREA Working Papers 202217, University of Barcelona, Research Institute of Applied Economics, revised Oct 2022.
- C. Ciocirlan & M. Nițoi, 2023. "Sovereign risk connectedness: the impact of ECB’s policy announcements in Central and Eastern Europe," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 50(4), pages 1025-1054, November.
- Liu, Peipei & Huang, Wei-Qiang, 2022. "Modelling international sovereign risk information spillovers: A multilayer network approach," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
- Zhizhen Chen & Guifen Shi & Boyang Sun, 2024. "Cross-border spillovers in G20 sovereign CDS markets: cluster analysis based on K-means machine learning algorithm and TVP–VAR models," Empirical Economics, Springer, vol. 67(6), pages 2463-2502, December.
- Choi, Sun-Yong, 2022. "Credit risk interdependence in global financial markets: Evidence from three regions using multiple and partial wavelet approaches," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
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