I want to measure the covariance structure of various asset returns based on varying investment periods. Campbell and Viceira (2005) do this, using known return predictors (i.e. dividend yield, yield spread,..) to describe asset returns with an AR(1) process. They find US stock and US gov bond correlations between close to 0 and up to 0.6 , depending on the lenght of the investment period.
However I want to analyse ex post return data (ranging from 1 month to at least 20 years), starting at 1969. Overlapping data will be used to get a (hopefully) sufficient amount of data points.
Now to my question: I am aware that rolling returns induce autocorrelation, this will effect the asset return volatility as well as the correlation coefficients. So I'm looking for correction methods to get rid of the auto correlation probelm. So far I have found something called Generalised Least Square (GLS) which seems to work for overlapping data in case of regression analysis. However I am not sure if this is really applicable in my case since I am measuring correlation coefficients.
Are there any corrections for overlapping sample autocorrelation when calculating asset return correlation coefficients?
As you might have noticed I am fairly new to the world of econometrics. I have only used excel so far, but am eager to learn R.