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Euro conversion and return dynamics of European financial markets: a frequency domain approach

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Abstract

Using a frequency domain approach, we compare the spectra of equity market index returns for the 12 Euro-zone countries, the UK, the US, and Japan, over several time frames before and after the introduction of the Euro. In the immediate aftermath of the Euro-introduction, we find a reduced volatility over all frequencies, in which no strong cyclical components are present. However, in the long run, equity markets exhibit a volatility increase: the larger European equity markets develop dynamics that exhibit strikingly similar patterns, while the smaller European equity markets appear to follow dynamics closely resembling white noise. The similarity in dynamics is a likely candidate to explain the increase in correlation among the European markets. Furthermore, the European equity markets initially exhibited dynamics that resembled those of the US, while after the introduction of the Euro, the dynamics of the European Markets exhibits patterns similar to those found the UK. This suggests a change in the dynamic interdependency between the UK and the European markets and an increased convergence of UK market behavior with that behavior dominant in the Euro-zone. The findings may provide important implications for investors seeking to take advantage of international diversification.

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Notes

  1. Using a GJR-GARCH-MA-t model, Bartram et al. (2007) find similar results. That is to say, the Euro-conversion has only enhanced the market dependency among the larger Euro-zone members, as well as the UK However, their approach does not reveal if the Euro has enhanced the efficiency within the individual countries’ equity markets.

  2. We examined all return series used in this study for unit root using the Kwaitoski, Philips, Schmidt and Shin (KPSS)-test. The null hypothesis that the return series are stationary could not be rejected.

  3. Comparing European equity indexes with those of the US and Japan raises the issue of time-synchronization, since the markets close at different times. However, this problem can be ignored since we compare the spectra pre- and post-Euro-introduction, which would introduce the same bias in all time-windows.

  4. We use the total return index (RI), which includes dividends as well as price changes. The indexes have the common base year in January 1, 1973. The question mark represents a substitute for the country’s synonym.

  5. Converting all the indexes to US dollar terms might weaken the examination in this study as it includes exchange rate movements (see also Smith 2001). For robustness, however, we examined all spectra using total return indexes denominated in US dollars (not reported). The main results did not change.

  6. To test for significance, we follow the methodology employed by Rausser and Cargill (1970). The tests consist of comparing the magnitude of each peak against those that would be obtained if the series were white noise. The ratio between the theoretical and the actual magnitude of the peak follows a χ 2 distribution with a number of degrees of freedom that depends upon the sample size and the width of the smoothing window employed (see Koopmans 1995, for additional explanations).

  7. One obtains the period of the cycle by dividing the total number of trading days in the sample by the number of cycles that fit in that sample. This value is reported in the abscissa of the plots. For example, for the 1-year period: 260 days/55 = 4.8 days per cycle. Given that some weeks have four trading days (or less), but most weeks have five trading days, a cycle that repeats every week would, on average, have a period between 4 and 5 days.

  8. We also analyzed the spectra analysis for the 3- and 4-year pre- and post-Euro-introduction window, which were very similar to the 5-year pre- and post-period. Thus, those graphs are not reported in this paper, but they are available from the authors upon request.

  9. To preserve space, we report only the graphs from seven of the 12 member countries. The other graphs are available from the authors upon request.

  10. Not reported in the Table, but available upon request.

  11. We report volatilities for the band 3.91 to 4.22 days and 3.77 to 4.37 days. However, we also considered alternative bands, which did not change the main results from those presented in Table 4 and 5.

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Correspondence to Axel Grossmann.

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Grossmann, A., Giudici, E. & Simpson, M.W. Euro conversion and return dynamics of European financial markets: a frequency domain approach. J Econ Finan 38, 1–26 (2014). https://doi.org/10.1007/s12197-011-9204-9

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