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High- and Low-Frequency Correlations in European Government Bond Spreads and Their Macroeconomic Drivers

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  • Simona Boffelli
  • Vasiliki D. Skintzi
  • Giovanni Urga

Abstract

We propose to adopt high-frequency DCC-MIDAS models to estimate high- and low-frequency correlations in the 10-year government bond spreads for Belgium, France, Italy, the Netherlands, and Spain relative to Germany, from June 1, 2007 to May 31, 2012. The high-frequency component, reflecting financial market conditions, is evaluated at 15-minute frequency, while the low-frequency component, fixed through a month, depends on country-specific macroeconomic conditions. We find strong links between spreads volatility and worsening macroeconomic fundamentals; in presence of similar macroeconomic fundamentals relative spreads move together; the increasing correlation in spreads during the burst of the sovereign debt crisis cannot be entirely ascribed to macroeconomic factors but rather to changes in market liquidity.

Suggested Citation

  • Simona Boffelli & Vasiliki D. Skintzi & Giovanni Urga, 2017. "High- and Low-Frequency Correlations in European Government Bond Spreads and Their Macroeconomic Drivers," Journal of Financial Econometrics, Oxford University Press, vol. 15(1), pages 62-105.
    • Handle: RePEc:oup:jfinec:v:15:y:2017:i:1:p:62-105.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbv023
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    References listed on IDEAS

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    1. Michael J. Fleming, 2003. "Measuring treasury market liquidity," Economic Policy Review, Federal Reserve Bank of New York, issue Sep, pages 83-108.
    2. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2016. "Testing for Granger causality with mixed frequency data," Journal of Econometrics, Elsevier, vol. 192(1), pages 207-230.
    3. Bollerslev, Tim & Russell, Jeffrey & Watson, Mark (ed.), 2010. "Volatility and Time Series Econometrics: Essays in Honor of Robert Engle," OUP Catalogue, Oxford University Press, number 9780199549498.
    4. repec:bla:jfinan:v:44:y:1989:i:5:p:1115-53 is not listed on IDEAS
    5. Attinasi, Maria Grazia & Checherita-Westphal, Cristina & Nickel, Christiane, 2009. "What explains the surge in euro area sovereign spreads during the financial crisis of 2007-09?," Working Paper Series 1131, European Central Bank.
    6. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.
    7. Suzanne S. Lee & Per A. Mykland, 2008. "Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics," The Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2535-2563, November.
    8. Mr. Ashoka Mody, 2009. "From Bear Stearns to Anglo Irish: How Eurozone Sovereign Spreads Related to Financial Sector Vulnerability," IMF Working Papers 2009/108, International Monetary Fund.
    9. Gros, Daniel, 2011. "External versus Domestic Debt in the Euro Crisis," CEPS Papers 5677, Centre for European Policy Studies.
    10. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.
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    Cited by:

    1. Buse, Rebekka & Schienle, Melanie, 2019. "Measuring connectedness of euro area sovereign risk," International Journal of Forecasting, Elsevier, vol. 35(1), pages 25-44.
    2. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    3. Hasan Isomitdinov & Vladimir Arčabić & Junsoo Lee & Youngjin Yun & James E. Payne, 2024. "International comovements of public debt," Economic Inquiry, Western Economic Association International, vol. 62(2), pages 722-747, April.
    4. Akyildirim, Erdinc & Corbet, Shaen & Nguyen, Duc Khuong & Sensoy, Ahmet, 2020. "Regulatory changes and long-run relationships of the EMU sovereign debt markets: Implications for future policy framework," International Review of Law and Economics, Elsevier, vol. 63(C).

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    Keywords

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    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • H63 - Public Economics - - National Budget, Deficit, and Debt - - - Debt; Debt Management; Sovereign Debt
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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