Hi I am analysing a data set of daily S&P 500 Index returns and my goal is to elaborate a relationship with a sentiment indicator (daily data). For this purpose I fitted a model to each variable. I found that a GARCH (1,1) suits best for the differenced closing price of the SPX and a GARCH (2,2) for the SPX returns. The sentiment indicator follows a ARMA (2,2) process. But now I am stuck. How do I use these fitted models to perform a linear regression on the variables? Without correction A Model like model=lm(spxclose-spxsentiment) is in my mind. But this simple method does not work with garch objects. The only two alternatives I tried were: A.one: Find the relationships by evaluating the cross correllograms: par(mfrow=c(2,2)) both<-ts.union(garchdspxclose$resid,arimaspxpcr$resid) acf(both,na.action = na.pass) pacf(both,na.action = na.pass) A.two: A paper mentions to correct with NeweyWest for autocorrelation and heteroskedasticity result <- dynlm(spxclose ~ lag(spxclose,1) +lag(spxpcr,1)+lag(vixpcr,1)) NeweyWest(result) coeftest(result, vcov = NeweyWest) Is this method also correcting for ARCH effects? Are VAR-modells or the cointegration from Granger and Engle appropriate tools to analysis daily exchange data comparing returns and sentiment indicators? Thank you for your help marco -- View this message in context: http://r.789695.n4.nabble.com/Use-fitted-Garch-models-in-linear-regression-tp4632648.html Sent from the R help mailing list archive at Nabble.com.