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Dependence Structures and Systemic Risk of Government Securities Markets in Central and Eastern Europe: A CoVaR-Copula Approach

Author

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  • Lu Yang

    () (School of Finance, Zhongnan University of Economics and Law, 182# Nanhu Avenue, East Lake High-Tech Development Zone, Wuhan 430-073, China)

  • Jason Z. Ma

    () (School of Finance, Zhongnan University of Economics and Law, 182# Nanhu Avenue, East Lake High-Tech Development Zone, Wuhan 430-073, China)

  • Shigeyuki Hamori

    () (Faculty of Economics, Kobe University, 2-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan)

Abstract

In this study, we proposed a new empirical method by combining generalized autoregressive score functions and a copula model with high-frequency data to model the conditional time-varying joint distribution of the government bond yields between Poland/Czech Republic/Hungary, and Germany. Capturing the conditional time-varying joint distribution of these bond yields allowed us to precisely measure the dependence of the government securities markets. In particular, we found a high dependence of these government securities markets in the long term, but a low dependence in the short term. In addition, we report that the Czech Republic showed the highest dependence with Germany, while Hungary showed the lowest. Moreover, we found that the systemic risk dynamics were consistent with the idea that the global financial crisis not only had spillover effects on countries with weak economic fundamentals (e.g., Hungary, which had the highest systemic risk), but also had contagion effects for both CEEC-3 countries and Germany. Finally, we confirm that three major market events, namely the EU accession, the global financial crisis, and the European debt crisis, caused structural changes to the dynamic correlation.

Suggested Citation

  • Lu Yang & Jason Z. Ma & Shigeyuki Hamori, 2018. "Dependence Structures and Systemic Risk of Government Securities Markets in Central and Eastern Europe: A CoVaR-Copula Approach," Sustainability, MDPI, Open Access Journal, vol. 10(2), pages 1-23, January.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:2:p:324-:d:128911
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    References listed on IDEAS

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    1. repec:gam:jsusta:v:10:y:2018:i:6:p:1809-:d:149805 is not listed on IDEAS
    2. repec:gam:jscscx:v:8:y:2019:i:6:p:173-:d:237540 is not listed on IDEAS
    3. repec:gam:jsusta:v:11:y:2019:i:5:p:1402-:d:211569 is not listed on IDEAS

    More about this item

    Keywords

    dynamic conditional correlation; generalized autoregressive score functions; time-varying copula function; CoVaR;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
    • Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

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