Greek sovereign crisis and European exchange rates: effects of news releases and their providers

Abstract

In this paper, we study the contribution of the geographical (both the regional and international) aspect of news releases, related to the Greek sovereign debt crisis. We investigate the impact of the Greek debt crisis via economic news and surprises on Euro exchange rate volatility and volatility-jumps within a Tobit regression framework. In particular, the impact of three categories of news is examined via the respective number of dummy variables, the number of news per day, and news surprises of 2-year, 5-year and 10-year government bonds and CDS. Also, the role of news releases providers is researched. The analysis starts from 1 July 2009 and ends on 31 May 2015. The data comprises intraday prices for (1) Euro to US dollar (Euro/USD), (2) Euro to Japanese yen (Euro/JPY), (3) Euro to Great British pound (Euro/GBP), (4) Euro to Swiss franc (Euro/CHF) and (5) Euro to Australian dollar (Euro/AUD). Our findings reveal that Greek events have an important impact on the behavior of the Euro exchange market. Secondly, the identity of the provider of news releases about the Greek sovereign crisis affects Europeś exchange rates highlighting homogeneity problems in the Eurozone. The uncertainty, introduced by the Greek sovereign crisis, as shown by our findings, signifies the financial instability in the Eurozone and the strength of the Euro currency. This is vital for policymakers in a common currency environment. Policymakers could evaluate such findings and re-examine the structural plan of the Euro, targeting primarily at the financial integration of the member states.

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Notes

  1. 1.

    See Bovi and Cerqueti (2016), Sensoy et al. (2018) and Sun et al. (2018), among others.

  2. 2.

    For a detailed analysis of the dynamics of the Greek debt crisis, see Ardagna and Caselli (2014).

  3. 3.

    See Arghyrou and Tsoukalas (2011).

  4. 4.

    Previous literature shows that the conditional mean adjustments of exchange rates to macroeconomic news occur quickly, within a few minutes following the news announcement, thus effectively amounting to jumps (Andersen et al. 2007). Therefore, in markets where return reaction to announcements is rapid (as is the case with FX markets), the use of wider return windows may contaminate the announcement effects, since longer intervals may include other events as well. This would reduce the public signal to noise ratio and introduce bias in the news response coefficients. To mitigate this bias, many studies on the FX literature use high frequency data to examine the announcement effects (Goldberg and Grisse 2013).

  5. 5.

    See for example Jiang et al. (2012) and Kutan et al. (2012).

  6. 6.

    Piccotti (2018) obtained data on pre-scheduled macroeconomic news events from FXStreet. In our study, we also cross-checked all events in Trading Economics and Bloomberg.

  7. 7.

    We follow the work of Patton (2011) in order to select the two most consistent estimators. For the comparison of estimation accuracy, we use the Quasi-Likelihood (QLIKE) loss function, as it leads to more accurate evaluations in rejecting inferior estimators. In particular, we measure accuracy using the QLIKE distance measure, based on squared open-to-close returns as a volatility benchmark/proxy, with one-day lead to break the dependence between estimation error in the realized measure and error in the proxy. The selection is based on the following eight classes of realized volatility estimators which are: naïve realized variance (Andersen et al. 2001a, b), bias-corrected for microstructure noise and optimally sampled realized variance (Bandi and Russell 2008), first-order autocorrelation-adjusted realized variance (Hansen and Lunde 2006), two-scale realized variance (Zhang et al. 2005), realized non-flat-top Parzen kernel (Barndorff-Nielsen et al. 2011), pre-averaged realized variance (Podolskij and Vetter 2009), realized range-based variance (Christensen and Podolskij 2007), maximum likelihood realized variance (Ait-Sahalia et al. 2005) and realized kernel estimator of Barndorff-Nielsen et al. (2009).

  8. 8.

    We mention at the case where the observations occur at equidistant time intervals \(\Delta \), in which case the parameter \(\sigma ^{2}\) is estimated at time T on the basis of \(N+1\) discrete observations recorded at times \(t_{0}=0\), \(t_{1}=\Delta \), ...,\(t_{N}=N\Delta =T\), see Ait-Sahalia et al. (2005).

  9. 9.

    This estimator performs better in high-frequency intraday data, due to covering higher number of observations.

  10. 10.

    Intensity and magnitude of volatility-jumps do not change significantly, when a 1% significance level has been employed.

  11. 11.

    Intensity and magnitude of volatility-jumps do not change significantly, when a 1% significance level has been employed.

  12. 12.

    The relative (aggregated) impacts are based on absolute raw impacts.

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Correspondence to Nikolaos Sariannidis.

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Gkillas, K., Vortelinos, D., Floros, C. et al. Greek sovereign crisis and European exchange rates: effects of news releases and their providers. Ann Oper Res (2019). https://doi.org/10.1007/s10479-019-03199-x

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Keywords

  • Greek news
  • Realized volatility
  • Jumps
  • Policy
  • Tobit regression framework