structural breaks in correlation

Hi folks,

I am trying to understand structural breaks in correlation using the 
strucchange package in R.
I am looking at a rolling window estimate of correlation (pearsons) to 
identify breaks and see if the underlying process has changed.


 > data(EuStockMarkets)
 > dax <- log(EuStockMarkets[,"DAX"])
 >  ftse <- log(EuStockMarkets[,"FTSE"])
 > dax.ret<-diff(dax)
 > ftse.ret<-diff(ftse)

rollingcor <- function(ret, width) {
T<-dim(ret)[1]
results<-1:(T-width)
     for (i in 1:(T-width)) {
    indx<-i+width
        results[i] <- cor(ret[i:indx,1],ret[i:indx,2] )
     }
     return(results)
   }

 >dax.ftse.cor<-rollingcor(cbind(dax.ret,ftse.ret),50)
 > ordcus<-efp(dax.ftse.cor~1,type="OLS-CUSUM")
 > plot(ordcus)


Is this the right way to test a rolling correlation estimate? And are 
there other tests that are recommended besides the cusum test?
Appreciate your help,

Thanks,
Krishna