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...ATE A NEW VECTOR OF LENGTH 7 z <- NULL length(z)=7 dz <- NULL dz2 <- NULL #STORE THE VALUES IN z z <- lx[1+(i-1)*7:(i)*7] #THEN DIFFERENCE THOSE #THIS IS r_t,i,m dz=diff(z) #SUM THIS UP AND STORE IT IN r, THIS IS r_t r[i] <- sum(dz) #SUM UP THE SQUARES AND STORE IT IN rv, THIS IS RV_t dz2 <- dz^2 rv[i] <- sum(dz2) #END THE LOOP } However, the window seems to expand for some reason, so z ends up being a much longer vector than it should be and full of NAs. Any help or advice is much appreciated. Aodh?n -- View this message in context: http://r.789695.n4.nabble.com/Br...

I am trying to write a loop to forecast realized volatility over successive days for the purpose of VaR prediction using the HAR-RV-CJ model which is as follows: log(RV_t+1) = ?_0 + ?_CD log(CV_t) + ?_CW log(CV_t-5) + ?_CM log(CV_t-22) + ?_JD log(J_t) + ?_JW J_t-5 + ?_JM J_t-22 + e_t where RV is realized volatility, CV is continuous volatility and J is the jump which is RV - CV, _t is subscript for time t, which is one day basically I know how to compute ex post C...

I am trying to write a loop to forecast realized volatility over successive days for the purpose of VaR prediction using the HAR-RV-CJ model which is as follows: log(RV_t+1) = ?_0 + ?_CD log(CV_t) + ?_CW log(CV_t-5) + ?_CM log(CV_t-22) + ?_JD log(J_t + 1) + ?_JW log(J_t-5 + 1) + ?_JM log(J_t-22 + 1) + e_t where RV is realized volatility, CV is continuous volatility and J is the jump which is RV - CV, _t is subscript for time t, which is one day basically I know ho...