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Measuring comovement in the time-frequency space

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  • António Rua

Abstract

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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Citations

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Cited by:
  1. António Rua & Luís Catela Nunes, 2012. "A wavelet-based assessment of market risk: The emerging markets case," Working Papers w201203, Banco de Portugal, Economics and Research Department.
  2. António Rua, 2011. "A wavelet approach for factor‐augmented forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 666-678, November.
  3. Rabeh Khalfaoui & Mohammed Boutahar, 2012. "Portfolio Risk Evaluation An Approach Based on Dynamic Conditional Correlations Models and Wavelet Multi-Resolution Analysis," AMSE Working Papers 1208, Aix-Marseille School of Economics, Marseille, France.
  4. Luís Aguiar-Conraria & Manuel M. F. Martins & Maria Joana Soares, 2011. "Synchronization of Economic Sentiment Cycles in the Euro Area: a time-frequency analysis," CEF.UP Working Papers 1105, Universidade do Porto, Faculdade de Economia do Porto.
  5. Svatopluk Kapounek & Jitka Pomenkova, 2012. "The Endogeneity of Optimum Currency Areas Criteria in the Context of Financial Crisis: Evidence from Time-Frequency Domain Analysis," MENDELU Working Papers in Business and Economics 2012-31, Mendel University in Brno, Faculty of Business and Economics.
  6. António Rua, 2011. "Money growth and inflation in the euro area: a time-frequency view," Working Papers w201122, Banco de Portugal, Economics and Research Department.
  7. Jesús Crespo-Cuaresma & Octavio Fernández-Amador, 2010. "Business cycle convergence in EMU: A first look at the second moment," FIW Working Paper series 054, FIW.
  8. Tiwari, Aviral Kumar & Oros, Cornel & Albulescu, Claudiu Tiberiu, 2014. "Revisiting the inflation–output gap relationship for France using a wavelet transform approach," Economic Modelling, Elsevier, vol. 37(C), pages 464-475.
  9. Zied Ftiti & Aviral Tiwari & Ibrahim Fatnassi, 2014. "Oil price and macroeconomy in India - An evolutionary cospectral coherence approach," Working Papers 2014-068, Department of Research, Ipag Business School.
  10. Yung-hsiang Ying & Koyin Chang & Ginny ju-ann Yang & Chen-hsun Lee, 2014. "Measuring co-movement of globalization and democratization in the time–frequency space," Economics Bulletin, AccessEcon, vol. 34(1), pages 206-219.
  11. Aviral Kumar Tiwari & Olaolu Richard Olayeni, 2013. "Oil prices and trade balance: A wavelet based analysis for India," Economics Bulletin, AccessEcon, vol. 33(3), pages 2270-2286.
  12. Aguiar-Conraria, LuI´s & Joana Soares, Maria, 2011. "Business cycle synchronization and the Euro: A wavelet analysis," Journal of Macroeconomics, Elsevier, vol. 33(3), pages 477-489, September.
  13. Akoum, Ibrahim & Graham, Michael & Kivihaho, Jarno & Nikkinen, Jussi & Omran, Mohammed, 2012. "Co-movement of oil and stock prices in the GCC region: A wavelet analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 385-394.
  14. Gazi Salah Uddin & Mohamed Arouri & Aviral Kumar Tiwari, 2014. "Co-movements between Germany and International Stock Markets: Some New Evidence from DCC-GARCH and Wavelet Approaches," Working Papers 2014-143, Department of Research, Ipag Business School.
  15. Zuzana KucÌŒerovaÌ & Jitka Pomenkova, 2014. "Financial and Trade Integration of Selected EU Regions: Dynamic Correlation and Wavelet Approach," MENDELU Working Papers in Business and Economics 2014-45, Mendel University in Brno, Faculty of Business and Economics.
  16. Caraiani, Petre, 2012. "Stylized facts of business cycles in a transition economy in time and frequency," Economic Modelling, Elsevier, vol. 29(6), pages 2163-2173.

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