Abstract
The introduction of Euro currency was a game-changing event intended to induce convergence of Eurozone business cycles on the basis of greater monetary and fiscal integration. The benefit of participating into a common currency area exceeds the cost of losing autonomy in national monetary policy only in case of cycle co-movement. However, synchronization was put back mainly due to country-specific differences and asymmetries in terms of trade and fiscal policies that became profound at the outset of the global financial crisis. As opposed to previous studies that are mostly based on linear correlation or causality modeling, we utilize the cross-wavelet coherence measure to detect and identify the scale-dependent time-varying (de)synchronization effects amongst Eurozone and the broad Euro area business cycles before and after the financial crisis. Our results suggest that the enforcement of an active monetary policy by the ECB during crisis periods could provide an effective stabilization instrument for the entire Euro area. However, as dynamic patterns in the lead-lag relationships of the European economies are revealed, (de)synchronization varies across different frequency bands and time horizons.
Acknowledgments
The first author is particularly grateful to Ramazan Gençay for valuable comments and discussions. We are also thankful to Yanqin Fan and Ramazan Gençay for kindly providing the code of their wavelet-based unit root test.
References
Afonso, A., and D. Furceri. 2009. “Sectoral Business Cycle Synchronization in the European Union.” Economics Bulletin 29 (4): 2996–3014.Search in Google Scholar
Aguiar-Conraria, L., and M. J. Soares. 2011. “Business Cycle Synchronization and the Euro: A Wavelet Analysis.” Journal of Macroeconomics 33 (3): 477–489.10.1016/j.jmacro.2011.02.005Search in Google Scholar
Aguiar-Conraria, L., and M. J. Soares. 2013. “The Continuous Wavelet Transform: Moving Beyond Uni and Bivariate Analysis.” Journal of Economic Surveys 28 (2): 344–375.10.1111/joes.12012Search in Google Scholar
Aguiar-Conraria, L., N. Azevedo, and M. J. Soares. 2008. “Using Wavelets to Decompose the Time–Frequency Effects of Monetary Policy.” Physica A: Statistical Mechanics and its Applications 387 (12): 2863–2878.10.1016/j.physa.2008.01.063Search in Google Scholar
Angeloni, I., and L. Dedola. 1999. “From the ERM to the Euro: New Evidence on Economic and Policy Convergence Among EU Countries.” ECB Working Paper No. 4.10.2139/ssrn.355142Search in Google Scholar
Artis, M. J., and W. Zhang. 1997. “International Business Cycles and the ERM: Is there a European Business Cycle?” International Journal of Finance and Economics 2 (1): 1–16.10.1002/(SICI)1099-1158(199701)2:1<1::AID-IJFE31>3.0.CO;2-7Search in Google Scholar
Artis, M., M. Marcelino, and T. Proietti. 2004. “Characterizing the Business Cycle for Accession Countries.” CEPR Discussion Paper N. 4457.10.2139/ssrn.547102Search in Google Scholar
Artis, M., T. Proietti, and M. Marcellino. 2005. “Business Cycles in the New EU Member Countries and their Conformity with the Euro Area.” Journal of Business Cycle Measurement Analysis 2: 7–42.10.1787/jbcma-2005-5km7v183wfr5Search in Google Scholar
Baxter, M., and M. Kouparitsas. 2005. “Determinants of Business Cycle Co-movement: A Robust Analysis.” Journal of Monetary Economics 52: 113–157.10.1016/j.jmoneco.2004.08.002Search in Google Scholar
Bekiros, S., and M. Marcellino. 2013. “The Multiscale Causal Dynamics of Foreign Exchange Markets.” Journal of International Money and Finance 33: 282–305.10.1016/j.jimonfin.2012.11.016Search in Google Scholar
Bergman, M. U. 2007. “How similar are European business cycles?” In Growth and Cycle in the Eurozone, edited by G. L. Mazzi, and G. Savio, Basingstoke, Hampshire, UK: Palgrave MacMillan.Search in Google Scholar
Bordo, M. D., and T. F. Helbing. 2011. “International Business Cycle Synchronization in Historical Perspective.” The Manchester School 79 (2): 208–238.10.1111/j.1467-9957.2010.02236.xSearch in Google Scholar
Breitung, J., and B. Candelon. 2006. “Testing for Short and Long-Run Causality: A Frequency Domain Approach.” Journal of Econometrics 132: 363–378.10.1016/j.jeconom.2005.02.004Search in Google Scholar
Camacho, M., G. Perez-Quiros, and L. Saiz. 2006. “Are European Business Cycles Close Enough to be Just One?” Journal of Economic Dynamics and Control 30: 1687–1706.10.1016/j.jedc.2005.08.012Search in Google Scholar
Camacho, M., G. Perez-Quiros, and L. Saiz. 2008. “Do European Business Cycles Look Like One?” Journal of Economic Dynamics and Control 32: 2165–2190.10.1016/j.jedc.2007.09.018Search in Google Scholar
Caraiani, P. 2013. “The Uncertain Unit Root in GDP and CPI: A Wavelet-Based Perspective.” Applied Economics Letters 20 (3): 297–299.10.1080/13504851.2012.697114Search in Google Scholar
Clark, T. E., and E. Wincoop. 2001. “Borders and Business Cycles.” Journal of International Economics 55: 59–85.10.1016/S0022-1996(01)00095-2Search in Google Scholar
Crowley, P. 2008. “One Money, Several Cycles? Evaluation of European Business Cycles using Model-Based Cluster Analysis.” Evaluation of European Business Cycles using Model-Based Cluster Analysis. Bank of Finland, Research Discussion Papers, 3/2008.Search in Google Scholar
Dickey, D. A., and W. A. Fuller. 1981. “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root.” Econometrica 49 (4): 1057–1072.10.2307/1912517Search in Google Scholar
Fan, Y., and R. Gençay. 2010. “Unit Root Tests with Wavelets.” Econometric Theory 26 (5): 1305–1331.10.1017/S0266466609990594Search in Google Scholar
Ferreira-Lopes, A., and A. M. Pina. 2011. “Business Cycles, Core, and Periphery in Monetary Unions: Comparing Europe and North America.” Open Economies Review 22 (4): 565–592.10.1007/s11079-009-9133-9Search in Google Scholar
Fidrmuc, J., and I. Korhonen. 2006. “Meta-Analysis of the Business Cycle Correlation Between the Euro Area and the CEECs.” Journal of Comparative Economics 34: 518–537.10.1016/j.jce.2006.06.007Search in Google Scholar
Forni, M., M. Hallin, M. Lippi, and L. Reichlin. 2000. “The Generalized Factor Model: Identification and Estimation.” Review of Economics and Statistics 82: 540–554.10.1162/003465300559037Search in Google Scholar
Furceri, D., and G. Karras. 2008. “Business Cycle Volatility and Country Size: Evidence for a Sample of OECD Countries.” Economics Bulletin 5 (3): 1–7.Search in Google Scholar
Gençay, R., and N. Gradojevic. 2011. “Errors-In-Variables Estimation with Wavelets.” Journal of Statistical Computation and Simulation 81 (11): 1545–1564.10.1080/00949655.2010.495073Search in Google Scholar
Gençay, R., and D. Signori. 2015. “Multi-Scale Tests for Serial Correlation.” Journal of Econometrics 184 (1): 62–80.10.1016/j.jeconom.2014.08.002Search in Google Scholar
Gençay, R., B. Whitcher, and F. Selçuk. 2001. “Differentiating Intraday Seasonalities Through Wavelet Multi-Scaling.” Physica A 289 (3–4): 543–556.10.1016/S0378-4371(00)00463-5Search in Google Scholar
Gençay, R., B. Whitcher, and F. Selçuk. 2002. An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. San Diego: Academic Press.10.1016/B978-012279670-8.50004-5Search in Google Scholar
Gençay, R., N. Gradojevic, F. Selcuk, and B. Whitcher. 2010. “Asymmetry of Information flow Between Volatilities Across Time Scales.” Quantitative Finance 10: 895–915.10.1080/14697680903460143Search in Google Scholar
Geweke, J. 1982. “Measurement of Linear Dependence and Feedback Between Multiple Time Series.” Journal of American Statistical Association 77: 304–324.10.1080/01621459.1982.10477803Search in Google Scholar
Grinsted, A., J. C. Moore, and S. Jevrejeva. 2004. “Application of the Cross Wavelet Transform and Wavelet Coherence to Geophysical Time Series.” Nonlinear Processes in Geophysics 11: 561–566.10.5194/npg-11-561-2004Search in Google Scholar
Hosoya, Y. 1991. “The Decomposition and Measurement of the Interdependency Between Second-Order Stationary Processes.” Probability Theory and Related Fields 88 (4): 429–444.10.1007/BF01192551Search in Google Scholar
Imbs, J. 2004. “Trade, Finance, Specialization, and Synchronization.” The Review of Economics and Statistics 86: 723–734.10.1162/0034653041811707Search in Google Scholar
Inklaar, R., and J. De Haan. 2001. “Is there Really a European Business Cycle? A Comment.” Oxford Economic Papers 53 (2): 215–220.10.1093/oep/53.2.215Search in Google Scholar
Inklaar, R., R. Jong-A-Pin, and J. de Haan. 2008. “Trade and Business Cycle Synchronization in OECD Countries – A Re-examination.” European Economic Review 52: 646–666.10.1016/j.euroecorev.2007.05.003Search in Google Scholar
Percival, D., and A. Walden. 2006. Wavelet Methods for Time Series Analysis. NY, USA: Cambridge University Press.Search in Google Scholar
Phillips, P. C., and P. Perron. 1988. “Testing for a Unit Root in Time Series Regression.” Biometrika 75: 335–346.10.1093/biomet/75.2.335Search in Google Scholar
Ramsey, J. 2002. “Wavelets in Economics and Finance: Past and Future.” Studies in Nonlinear Dynamics and Econometrics 6: 1–27.Search in Google Scholar
Ramsey, J. B., and C. Lampart. 1998a. “The Decomposition of Economic Relationships by Time Scale Using Wavelets: Money and Income.” Macroeconomic Dynamics 2: 49–71.10.1017/S1365100598006038Search in Google Scholar
Ramsey, J. B., and C. Lampart. 1998b. “The Decomposition of Economic Relationships by Time Scale Using Wavelets: Expenditure and Income.” Studies in Nonlinear Dynamics & Econometrics 3 (1): 23–42.10.2202/1558-3708.1039Search in Google Scholar
Ramsey, J. B., D. Usikov, and G. M. Zaslavsky. 1995. “An Analysis of U.S. Stock Price Behavior Using Wavelets.” Fractals 3 (2): 377–389.10.1142/S0218348X95000291Search in Google Scholar
Rose, A. K. 2000. “One Money, One Market: Estimating the Effect of Common Currencies on Trade.” Economic Policy 30: 7–33.10.1111/1468-0327.00056Search in Google Scholar
Rose, A., and C. Engel. 2002. “Currency Unions and International Integration.” Journal of Money, Credit and Banking 34: 1067–1089.10.1353/mcb.2002.0058Search in Google Scholar
Rua, A. 2010. “Measuring Comovement in the Time-Frequency Space.” Journal of Macroeconomics 32: 685–691.10.1016/j.jmacro.2009.12.005Search in Google Scholar
Rua, A., and L. C. Nunes. 2009. “International Co-movement of Stock Returns: A Wavelet Analysis.” Journal of Empirical Finance 16 (4): 632–639.10.1016/j.jempfin.2009.02.002Search in Google Scholar
Torrence, C., and G. P. Compo. 1998. “A Practical Guide to Wavelet Analysis.” Bulletin of the American Meteorological Society 79: 605–618.10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2Search in Google Scholar
Torrence, C., and P. Webster. 1999. “Interdecadal Changes in the ESNOM on Soon System.” Journal of Climate 12: 2679–2690.10.1175/1520-0442(1999)012<2679:ICITEM>2.0.CO;2Search in Google Scholar
Wynne, M. A., and J. Koo. 2000. “Business Cycles under Monetary Union: A Comparison of the EU and US.” Economica 67: 347–374.10.1111/1468-0335.00213Search in Google Scholar
Xue, Y., R. Gençay, S. Fagan. 2013. “Jump Detection with Wavelets for High-Frequency Financial Time Series.” Quantitative Finance 14 (8): 1427–1444.10.1080/14697688.2013.830320Search in Google Scholar
Zivot, E., and D. Andrews. 1992. “Further Evidence on the Great Crash, The Oil Price Shock, and the Unit Root Hypothesis.” Journal of Business and Economic Statistics 10: 251–270.Search in Google Scholar
Supplemental Material
The online version of this article (DOI: 10.1515/snde-2014-0055) offers supplementary material, available to authorized users.
©2015 by De Gruyter
Articles in the same Issue
- Frontmatter
- Fourier inversion formulas for multiple-asset option pricing
- Particle Gibbs with ancestor sampling for stochastic volatility models with: heavy tails, in mean effects, leverage, serial dependence and structural breaks
- Testing the relationships between shadow economy and unemployment: empirical evidence from linear and nonlinear tests
- Business cycle (de)synchronization in the aftermath of the global financial crisis: implications for the Euro area
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Articles in the same Issue
- Frontmatter
- Fourier inversion formulas for multiple-asset option pricing
- Particle Gibbs with ancestor sampling for stochastic volatility models with: heavy tails, in mean effects, leverage, serial dependence and structural breaks
- Testing the relationships between shadow economy and unemployment: empirical evidence from linear and nonlinear tests
- Business cycle (de)synchronization in the aftermath of the global financial crisis: implications for the Euro area
- Amplitude and phase synchronization of European business cycles: a wavelet approach
- On the relationship between oil and gold before and after financial crisis: linear, nonlinear and time-varying causality testing
- Stock market’s reaction to money supply: a nonparametric analysis