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Economic volatility and sovereign yields’ determinants: a time-varying approach

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Abstract

Using monthly data for 10 euro area countries between 1999:01 and 2015:12, we take a new three-step methodological approach: first, we inspect the key determinants of 10-year government bond yield spreads; second, we compute country-specific time-varying coefficient models of spreads’ determinants; third, we use these estimates as explanatory variables in panel regressions using output volatility as the dependent variable. We find that better fiscal positions or higher-than-expected economic growth prospects reduce the yield spreads, while increases in the VIX, bid-ask spread, debt-to-GDP ratio or real effective exchange rate appreciation increase the spreads. Moreover, the responsiveness of the yield spread determinants increased in the run-up to the global financial crisis. Finally, for the case of the budget balance and real growth (bid-ask spread, debt-to-GDP ratio, real effective exchange rate and VIX), the larger (higher) in absolute value the corresponding spread’s responsiveness, the lower (higher) the economic volatility.

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Fig. 1

Note: Output volatility measured as the centred 3-year standard deviation of rolling real GDP growth. Sovereign bond yield spreads relative to Germany’s

Fig. 2

Note: The horizontal axis represents years. The figure displays the average spread across the different countries in the sample

Fig. 3

Note: “median” denotes the average (median) value across all country estimates; “pctile_25” and “pctile_75” denote the first and third quartiles of the distribution of time-varying estimates across all countries, respectively

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Notes

  1. Ludwig (2014b) and Sibbertsen et al. (2014), for example, investigated the stochastic trending behaviour of the spreads. However, such analysis goes beyond the scope of this paper.

  2. The approach proposed by Schlicht (2003) is very similar to that used by Aghion and Marinescu (2008). The main difference is in the computation of the variances \( \sigma_{i}^{2} \). Aghion and Marinescu (2008) use the Markov Chain Monte Carlo (MCMC) method to approximate these variances, while Schlicht (2003) uses a method-of-moments estimator.

  3. We also computed two additional versions of the relative standard deviation using instead the detrended real GDP (from HP and WEO). We also checked both the standard deviation (baseline) measure and the relative counterpart for autocorrelation using the Q-Lung-box test. Using a rolling window may create autocorrelation, but the Q-Lung-box test systematically rejected the null of no first-order autocorrelation, suggesting that there is no autocorrelation. We also tried replacing our baseline proxy of output volatility (based on the simple standard deviation) with an alternative based on a GARCH model of the quarterly real GDP series. Results are kept qualitatively unchanged.

  4. We are aware of literature on the political economy determinants of government bond pricing. For instance, Huang et al. (2015) and Duyvesteyn et al. (2016) discuss the relevance of political risk in affecting government bonds. Exploring such aspects in greater detail goes beyond the scope of this paper. In addition, we also rely on a political economy proxy as instrument in the robustness section below when estimating using a two-stage least squares estimator.

  5. We thank the editor and an anonymous referee for these suggestions.

  6. The country list includes: Austria, Belgium, Finland, France, Greece, Ireland, Italy, Netherlands, Portugal and Spain.

  7. Note that we tested the time-series properties of the set of variables employed in the empirical section (results are omitted for reasons of parsimony) and found evidence pointing to panel stationarity. We relied on Im–Pesaran–Shin and the Maddala–Wu tests.

  8. Note that when estimating Eq. (1) with VIX included, time fixed effects are dropped. VIX captures the global factor that only varies over time but not across units.

  9. Removing Greece and Portugal increases the slope of the fitted line to 0.55 but lowers the R-square to 0.14.

  10. The reason for why X in Eq. (2) is a scalar in the TVC regressions relates to the simple fact that we want to specifically isolate the impact of a given variable. Alternatively, one could have estimated the TVC with all the regressors included simultaneously. Doing so does not qualitatively change the main results.

  11. We thank an anonymous referee for this suggestion.

  12. We thank the editor for this point.

  13. Moreover, we could explore yet additional variables that are known to affect output volatility such as the effect of oil price changes (in line with Wegener et al. 2016). However, taking our panel of countries, first none is an oil exporting country and, second any common exogenous effect is econometrically captured by the time effects.

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Funding

UECE is supported by FCT (Fundação para a Ciência e a Tecnologia, Portugal).

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Authors and Affiliations

  1. REM – Research in Economics and Mathematics, UECE – Research Unit on Complexity and Economics, ISEG-UL – Universidade de Lisboa, R. Miguel Lupi 20, 1249-078, Lisbon, Portugal

    António Afonso

  2. UECE – Research Unit on Complexity and Economics, Centre for Globalization and Governance, Nova School of Business and Economics, Campus Campolide, 1099-032, Lisbon, Portugal

    João Tovar Jalles

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  1. António Afonso
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Correspondence to João Tovar Jalles.

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We thank the editor and an anonymous referee for very useful comments and suggestions. The opinions expressed herein are those of the authors and do not necessarily reflect those of their employers.

Appendix

Appendix

See Tables 9, 10 and 11.

Table 9 Summary statistics monthly data
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Table 10 Data definition and sources
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Table 11 Summary statistics annual data
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Afonso, A., Jalles, J.T. Economic volatility and sovereign yields’ determinants: a time-varying approach. Empir Econ 58, 427–451 (2020). https://doi.org/10.1007/s00181-018-1540-6

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