IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-02353094.html

Spillover across Eurozone credit market sectors and determinants

Author

Listed:
  • Syed Jawad Hussain Shahzad

    (Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School)

  • Elie Bouri

    (USEK School of Business - Holy-Spirit University of Kaslik [Jounieh] / Université Saint-Esprit de Kaslik)

  • Jose Arreola-Hernandez

    (Rennes SB - Rennes School of Business)

  • David Roubaud

    (Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School)

  • Stelios Bekiros

    (Department of Economics - EUI - European University Institute - Institut Universitaire Européen)

Abstract

We examine spillover and its determinants among Eurozone sector level credit markets using time and frequency domain spillover approaches. Based on network theory and connectedness analysis, we identify the sectors that are major transmitters and receivers of spillover during normal and crisis periods. The rolling window analysis shows that short-run spillover among credit market sectors intensifies during global and Eurozone crisis periods. Further, using Bayesian model averaging, we find that overall financial conditions and stock market volatility are the main drivers of total and sector-level spillover. Our findings have important implications for policymakers and investors interested in Euro-area credit risk at the sector level.

Suggested Citation

  • Syed Jawad Hussain Shahzad & Elie Bouri & Jose Arreola-Hernandez & David Roubaud & Stelios Bekiros, 2019. "Spillover across Eurozone credit market sectors and determinants," Post-Print hal-02353094, HAL.
    • Handle: RePEc:hal:journl:hal-02353094
    DOI: 10.1080/00036846.2019.1619014
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bouri, Elie & Cepni, Oguzhan & Gabauer, David & Gupta, Rangan, 2021. "Return connectedness across asset classes around the COVID-19 outbreak," International Review of Financial Analysis, Elsevier, vol. 73(C).
    2. Abuzayed, Bana & Bouri, Elie & Al-Fayoumi, Nedal & Jalkh, Naji, 2021. "Systemic risk spillover across global and country stock markets during the COVID-19 pandemic," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 180-197.
    3. Huang, Wei-Qiang & Liu, Peipei, 2023. "Cross-market risk spillovers among sovereign CDS, stock, foreign exchange and commodity markets: An interacting network perspective," International Review of Financial Analysis, Elsevier, vol. 90(C).
    4. Bouri, Elie & Lei, Xiaojie & Jalkh, Naji & Xu, Yahua & Zhang, Hongwei, 2021. "Spillovers in higher moments and jumps across US stock and strategic commodity markets," Resources Policy, Elsevier, vol. 72(C).
    5. Li, Wei-Zhen & Zhai, Jin-Rui & Jiang, Zhi-Qiang & Wang, Gang-Jin & Zhou, Wei-Xing, 2022. "Predicting tail events in a RIA-EVT-Copula framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    6. Cesario Mateus & Miramir Bagirov & Irina Mateus, 2024. "Return and volatility connectedness and net directional patterns in spillover transmissions: East and Southeast Asian equity markets," International Review of Finance, International Review of Finance Ltd., vol. 24(1), pages 83-103, March.
    7. Won Joong Kim & Gunho Jung & Sun-Yong Choi, 2020. "Forecasting CDS Term Structure Based on Nelson–Siegel Model and Machine Learning," Complexity, Hindawi, vol. 2020, pages 1-23, July.
    8. Mudassar Hasan & Muhammad Abubakr Naeem & Muhammad Arif & Syed Jawad Hussain Shahzad & Xuan Vinh Vo, 2022. "Liquidity connectedness in cryptocurrency market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    9. Haithem Awijen & Younes Ben Zaied & Ahmed Imran Hunjra, 2023. "Systematic and Unsystematic Determinants of Sectoral Risk Default Interconnectedness," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 561-587, August.
    10. Ren, Yi-Shuai & Klein, Tony & Jiang, Yong & Liu, Pei-Zhi & Weber, Olaf, 2025. "Dynamic connectedness between crude oil futures and energy industrial bond credit spread: Evidence from China," Energy Economics, Elsevier, vol. 143(C).
    11. Huynh, Toan Luu Duc & Foglia, Matteo & Doukas, John A., 2022. "COVID-19 and Tail-event Driven Network Risk in the Eurozone," Finance Research Letters, Elsevier, vol. 44(C).
    12. Siniša Bogdan & Natali Brmalj & Elvis Mujačević, 2023. "Impact of Liquidity and Investors Sentiment on Herd Behavior in Cryptocurrency Market," IJFS, MDPI, vol. 11(3), pages 1-17, July.
    13. Liew, Ping-Xin & Lim, Kian-Ping & Goh, Kim-Leng, 2022. "The dynamics and determinants of liquidity connectedness across financial asset markets," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 341-358.
    14. Ibhagui, Oyakhilome, 2021. "How do sovereign risk, equity and foreign exchange derivatives markets interact?," Economic Modelling, Elsevier, vol. 97(C), pages 58-78.
    15. Ordu-Akkaya, Beyza Mina & Özyıldırım, Süheyla, 2025. "Commodity dependence: Providing information on emerging market CDS spreads when economic indicators are absent," Emerging Markets Review, Elsevier, vol. 67(C).
    16. Ying-Ying Shen & Zhi-Qiang Jiang & Jun-Chao Ma & Gang-Jin Wang & Wei-Xing Zhou, 2022. "Sector connectedness in the Chinese stock markets," Empirical Economics, Springer, vol. 62(2), pages 825-852, February.
    17. 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).
    18. Ren, Yi-Shuai & Klein, Tony & Jiang, Yong, 2025. "Unveiling the asymmetric dynamic spillovers in industry bond credit risk: Is the energy industry the prime mover?," International Review of Financial Analysis, Elsevier, vol. 101(C).
    19. 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.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-02353094. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.