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Correlation measures how much two series move together over a fixed interval. Are there any techniques that measure co-movement over a variable time frame? One technique I am aware of is concordance.

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Are you referring to Kendall's W? –  chrisaycock May 18 '12 at 12:46
    
Well, really correlation has nothing to do with the data points coming from time series realizations. You may do better looking through the literature for the (numerous) treatments of cointegration. –  Brian B May 18 '12 at 19:48
    
Could you be clearer? Are you talking about irregularly spaced time series? –  Zarbouzou May 19 '12 at 14:46
    
Thanks for the advice. I will look into these areas further - I have Carol ALexanders text and another by Malevergne which I hope will help. –  papdog Jun 13 '12 at 8:39
    
It would be better to know if two series are synchronous/asynchronous in time? If its asynchronous, then you could try using Hayashi-Yoshida estimator. –  shoonya Feb 5 '13 at 7:03
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1 Answer

  1. One approach is to simply calculate the correlations over a rolling window. Changing the window can provide an indication as to how the correlations are changing over time.
  2. A more sophisticated approach is to estimate a DCC-Copula model. This type of model beras some similarity with Garch in its structure, except that it is for the paremeters of a copula model (I believe they were an adjustment to initial work done on multivariate DCC Garch models). This type of model could also provide you with contemporaneous estimates of conditional correlations. There is a user-created Matlab toolbox that can estimate these models (site: http://www.mathworks.com/matlabcentral/fileexchange/29303-dynamic-copula-toolbox-3-0). While it is user-friendly, you may need to adapt it to suit your needs...
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I'm not sure this is entirely what the OP is asking, but it seems like it is... –  John May 18 '12 at 22:09
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