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Measuring comovement in the time-frequency space

  • António Rua

The measurement of comovement among variables has a long tradition in the economic and financial literature. Traditionally, comovement is assessed in the time domain through the well-known correlation coefficient while the evolving properties are investigated either through a rolling window or by considering non-overlapping periods. More recently, Croux, Forni and Reichlin [Review of Economics and Statistics 83 (2001)] have proposed a measure of comovement in the frequency domain. While it allows to quantify the comovement at the frequency level, such a measure disregards the fact that the strength of the comovement may vary over time. Herein, it is proposed a new measure of comovement resorting to wavelet analysis. This wavelet-based measure allows one to assess simultaneously the comovement at the frequency level and over time. In this way, it is possible to capture the time and frequency varying features of comovement within a unified framework which constitutes a refinement to previous approaches.

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Paper provided by Banco de Portugal, Economics and Research Department in its series Working Papers with number w201001.

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Date of creation: 2010
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Handle: RePEc:ptu:wpaper:w201001
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  1. Christophe Croux & Mario Forni & Lucrezia Reichlin, 2001. "A measure of co-movement for economic variables: theory and empirics," ULB Institutional Repository 2013/10139, ULB -- Universite Libre de Bruxelles.
  2. Rua, António & Nunes, Luís C., 2009. "International comovement of stock market returns: A wavelet analysis," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 632-639, September.
  3. repec:ner:tilbur:urn:nbn:nl:ui:12-194296 is not listed on IDEAS
  4. Viviana Fernandez, 2006. "The International CAPM and a Wavelet-Based Decomposition of Value at Risk," NBER Working Papers 12233, National Bureau of Economic Research, Inc.
  5. Lemmens, A. & Croux, C. & Dekimpe, M.G., 2007. "Consumer confidence in Europe : United in diversity," Other publications TiSEM ea8c3268-2c0b-4fcc-9d4a-6, Tilburg University, School of Economics and Management.
  6. António Rua & Luís Catela Nunes, 2003. "Coincident and Leading Indicators for the Euro Area: A Frequency Band Approach," Working Papers w200307, Banco de Portugal, Economics and Research Department.
  7. Ramsey James B. & Lampart Camille, 1998. "The Decomposition of Economic Relationships by Time Scale Using Wavelets: Expenditure and Income," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(1), pages 1-22, April.
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  10. Mauro Gallegati & Antonio Palestrini & Milena Petrini, 2008. "Cyclical Behavior Of Prices In The G7 Countries Through Wavelet Analysis," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 119-130.
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  17. Yogo, Motohiro, 2008. "Measuring business cycles: A wavelet analysis of economic time series," Economics Letters, Elsevier, vol. 100(2), pages 208-212, August.
  18. Breitung, Jorg & Candelon, Bertrand, 2006. "Testing for short- and long-run causality: A frequency-domain approach," Journal of Econometrics, Elsevier, vol. 132(2), pages 363-378, June.
  19. Theodore M. Crone, 2005. "An Alternative Definition of Economic Regions in the United States Based on Similarities in State Business Cycles," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 617-626, November.
  20. Kim, Sangbae & In, Francis, 2005. "The relationship between stock returns and inflation: new evidence from wavelet analysis," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 435-444, June.
  21. Eickmeier, Sandra & Breitung, Jorg, 2006. "How synchronized are new EU member states with the euro area? Evidence from a structural factor model," Journal of Comparative Economics, Elsevier, vol. 34(3), pages 538-563, September.
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