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Time-varying contemporaneous spillovers during the European Debt Crisis

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Abstract

This paper considers contemporaneous spillover effects between Germany and four peripheral European countries that were most affected by the European Debt Crisis, and provides evidence of bidirectional spillovers among these equity markets. We document that there is asymmetry and time variation in contemporaneous spillovers. Particularly, contemporaneous return spillovers from Germany to the peripheral equity markets is higher than the other way around. We show that European Debt Crisis led to a decrease in the contemporaneous spillover effects.

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Notes

  1. The European Financial Stability Facility program was created as a temporary solution to the EDC. Starting from October 2012, the European Stability Mechanism is the permanent rescue mechanism that safeguards financial stability in Europe by providing financial assistance to the European countries. The ECB’s policies refer to its decision to purchase the government debt of the troubled EA countries under its Securities Markets Program, adopted in May 2010 and replaced by the Outright Monetary Transactions program in October 2012.

  2. An alternative approach to examine time variation in stock market interdependence builds on the work of Manner and Candelon (2010), who use copulas to capture stock market interdependence and a sequential breakpoint test algorithm to identify time variation in interdependence.

  3. There are several reasons for the choice of the German equity market and GIPS equity markets. First, these markets are integrated and related through trade, banking system and debt holdings which facilitate the transmission of shocks among them, especially during the European crisis (Stracca 2015). For instance, German banks have invested heavily in Greek bonds. As such, it is important to investigate whether or not the magnitude of the spillover effects has changed with the ongoing EDC. Second, Ehrmann and Fratzscher (2017) show there are relatively little spillovers in bond yields among the peripheral countries (Greece, Italy, Portugal, Spain and Ireland), except the bidirectional spillovers between Italy and Spain. They document that bond yields of the peripheral countries strongly co-move with the German bond market, and are also more affected by shocks to their own bond market. Third, Germany is an important member of the European Union which has been less affected by the EDC and has highly contributed to the European Financial Stability Facility program (now European Stability Mechanism). This has led to an increase in its influence with regard to the implementation of different policies across the Euro Area. These policies (e.g., the financial support programs, OMT program) have affected and have mainly focused on the GIPS countries, the origin of the debt crisis. In addition, the GIPS’s credit ratings have been downgraded several times between 2010 and 2012. These credit rating downgrades might negatively affect their stock markets as well as the German stock market. As such, it is essential to explore the relations between the German and GIPS returns and also to what extent the EDC has influenced them. Specifically, it is relevant to examine to what extent the GIPS markets moved away from Germany and the other way around.

  4. Belgium, Netherlands, Finland, Austria, France, Ireland and GIPS.

  5. Their analysis includes three core countries (Germany, France and the Netherlands) and five peripheral countries of the EA (GIPS and Ireland).

  6. See also, the studies of Antonakakis and Vergos (2013) and of Claeys and Vasicek (2014) who use this method of a VAR model proposed by Diebold and Yilmaz (2012). The study of Louzis (2015) considers the EONIA rate, EUR/USD exchange rate, Ireland and GIPS bond markets and the equity markets in GIPS countries, Ireland, France, Belgium, Austria, Netherland, US and Germany.

  7. Their investigation includes GIPS, Ireland, France, UK and Germany.

  8. Countries included Germany, France, UK and peripheral counties, i.e., GIPS and Ireland.

  9. In line with Cappiello et al. (2006) the German equity market can be seen as the benchmark of the EA equity markets. Moreover, Germany is one of the major European contributors to the financial assistance programs. Additionally, Germany is a leading member of EA with an influential role regarding the European politics (e.g., the implementation of austerity measures), especially during the EDC.

  10. The use of a large multivariate specification encapsulating all markets would be ideal. However, estimation of such a large system is quite cumbersome, even more so when estimating the system over rolling windows to obtain time-varying contemporaneous spillovers, where we could expect no convergence in the likelihood due to the size of the model. This motivates us to focus on bivariate systems where we model equity returns from the peripheral countries vis-a-vis the German market.

  11. Various methods have been put forward to use heteroskedasticity in the data for identification of structural parameters. Rigobon (2003) uses volatility regimes over different periods of time, while Ehrmann et al. (2011) and Andersen et al. (2007) use a rolling-windows approach to identify volatility regimes. Lütkepohl’s (2013) further suggests an approach based on a GARCH model, and a Markov-switching model to capture heterogeneity in the data. While all these approaches can potentially be used to achieve identification of the structural parameters, the approach of Lanne and Lütkepohl (2010) seems most flexible in our setting as we estimate the model on a rolling window to extract time-varying parameters.

  12. Note that the assumption of time invariance only applies to the estimation window. When we take the model to the data, we estimate the structural parameters over various subsamples (pre- and post-crisis) and on the basis of rolling windows to introduce time variation.

  13. This frequency minimizes the effects of non-synchronous data which may arise when a market is closed in one country, while another market is open in another country. Moreover, the weekly frequency is characterized by less noise and is able to better analyze the transmission of return shocks over time and during financial crises.

  14. The start date for the GFC is in line with the studies of Ait-Sahalia et al. (2012), De Santis (2014), Gjika and Horvath (2013), Mierau and Mink (2013) and Syllignakis and Kouretas (2011).

  15. In addition, we assess the stability and statistical significance of contemporaneous relations using the breakpoint test based on Qu and Perron (2007), Blatt et al. (2015), and Bataa et al. (2013) (we report the Wald-type statistic as per Equation (4) of Bataa et al. (2013) as our break dates are known). “Appendix 1” reports these statistics. We show that generally the null hypotheses of constant contemporaneous spillover effects can be rejected for the structural breaks due to both GFC and EDC. In sum, Wald’s test emphasizes the relevance of considering contemporaneous relations over the pre-GFC, GFC, \(\text {EDC}^\text {first phase}\) and \(\text {EDC}^\text {second phase}\) periods.

  16. As a robustness check, we also use a window of 78 observations (a period of one and a half year). We find that the results are very similar to those presented in this paper.

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Authors and Affiliations

  1. Sim Kee Boon Institute for Financial Economics, Lee Kong Chian School of Business, Singapore Management University, Administration Building, 81 Victoria Street, Singapore, 188065, Singapore

    Marinela Adriana Finta

  2. Department of Finance, Auckland University of Technology, Private Bag 92006, Auckland, 1020, New Zealand

    Bart Frijns & Alireza Tourani-Rad

Authors
  1. Marinela Adriana Finta
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  2. Bart Frijns
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  3. Alireza Tourani-Rad
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Corresponding author

Correspondence to Marinela Adriana Finta.

Additional information

We thank participants at the 2017 Annual Conference of the Multinational Finance Society (MFS), 2017 Annual Conference of the Romanian Academic Economists from Abroad (ERMAS), 2016 Auckland Finance Meeting, 2016 SIRCA’s Pitching Research Symposium, the 2016 seminar at Queen’s University Belfast, the 2016 seminar Technical University of Dortmund and the 2016 seminar at University of Liverpool for helpful comments and suggestions.

Appendices

Appendix 1

See Table 5.

Table 5 Wald’s test
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Appendix 2

See Table 6.

Table 6 Correlation matrix between returns
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Finta, M.A., Frijns, B. & Tourani-Rad, A. Time-varying contemporaneous spillovers during the European Debt Crisis. Empir Econ 57, 423–448 (2019). https://doi.org/10.1007/s00181-018-1480-1

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