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Wavelet multiple correlation and cross-correlation: A multiscale analysis of euro zone stock markets

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  • Fernández Macho, Francisco Javier

Abstract

Statistical studies that consider multiscale relationships among several variables use wavelet correlations and cross-correlations between pairs of variables. This procedure needs to calculate and compare a large number of wavelet statistics. The analysis can then be rather confusing and even frustrating since it may fail to indicate clearly the multiscale overall relationship that might exist among the variables. This paper presents two new statistical tools that help to determine the overall correlation for the whole multivariate set on a scale-by-scale basis. This is illustrated in the analysis of a multivariate set of daily Eurozone stock market returns during a recent period. Wavelet multiple correlation analysis reveals the existence of a nearly exact linear relationship for periods longer than the year, which can be interpreted as perfect integration of these Euro stock markets at the longest time scales. It also shows that small inconsistencies between Euro markets seem to be just short within-year discrepancies possibly due to the interaction of different agents with different trading horizons. On the other hand, multiple cross-correlation analysis shows that the French CAC40 may lead the rest of the Euro markets at those short time scales.

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Bibliographic Info

Paper provided by Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística) in its series BILTOKI with number 2011-04.

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Date of creation: Jun 2011
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Handle: RePEc:ehu:biltok:201104

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Postal: Dpto. de Econometría y Estadística, Facultad de CC. Económicas y Empresariales, Universidad del País Vasco, Avda. Lehendakari Aguirre 83, 48015 Bilbao, Spain
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Keywords: Euro zone; MODWT; multiscale analysis; multivariate analysis; stock markets; returns; wavelet transform;

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  1. Dionisio, Andreia & Menezes, Rui & Mendes, Diana A., 2007. "On the integrated behaviour of non-stationary volatility in stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 58-65.
  2. Jussi Nikkinen & Seppo Pynnönen & Mikko Ranta & Sami Vähämaa, 2011. "Cross‐dynamics of exchange rate expectations: a wavelet analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 16(3), pages 205-217, 07.
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  4. Hardouvelis, Gikas A & Malliaropoulos, Dimitrios & Priestley, Richard, 1999. "EMU and European Stock Market Integration," CEPR Discussion Papers 2124, C.E.P.R. Discussion Papers.
  5. Aguiar-Conraria, Luís & Azevedo, Nuno & Soares, Maria Joana, 2008. "Using wavelets to decompose the time–frequency effects of monetary policy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 2863-2878.
  6. Fratzscher, Marcel, 2001. "Financial market integration in Europe: on the effects of EMU on stock markets," Working Paper Series 0048, European Central Bank.
  7. Jian Yang & Insik Min & Qi Li, 2003. "European Stock Market Integration: Does EMU Matter?," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 30(9-10), pages 1253-1276.
  8. Gallegati, Marco, 2008. "Wavelet analysis of stock returns and aggregate economic activity," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3061-3074, February.
  9. Bartram, Sohnke M. & Taylor, Stephen J. & Wang, Yaw-Huei, 2007. "The Euro and European financial market dependence," Journal of Banking & Finance, Elsevier, vol. 31(5), pages 1461-1481, May.
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Cited by:
  1. Benhmad, François, 2013. "Bull or bear markets: A wavelet dynamic correlation perspective," Economic Modelling, Elsevier, vol. 32(C), pages 576-591.

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