Hi Megh,
As a practitioner I would use something like
x1 <- get.hist.quote(instrument = "^gspc", start = "1990-01-01")
x2 <- get.hist.quote(instrument = "^dji", start = "1990-01-01") ##
both need to be synchronized in time
r1 <- log(x1[, 2])-log(x1[, 3]) ## range as proxy for vola
r2 <- log(x2[, 2])-log(x2[, 3]) ## not ()^2 to avoid possibly
non-finite fourth moment
r <- merge(r1, r2)
plot(r)
rcor <- rollapply(r, width = 250, FUN = function(z) cor(z[, 1], z[,
2], method = "pearson"),
by.column = FALSE, align = "left") ## method !=
"pearson" for rank correlations
plot(rcor)
as a starting point. As a next step I would use a better proxy for
vola from the zoo of realized vola based estimators.
Best regards
Adrian
Dear all, I was trying to understand the correlation among
the?volatilities?in different financial market, however am in dilemma
what could be the rightful and acceptable-to-everyone approach. I
thought to estimate the volatilities of?individual?markets using some
GARCH modeling, then just calculate the correlation coefficient on
the estimated time series of estimated daily volatilities.?
Is it correct approach to understand the correlation? Can somebody
point me any online paper or any idea on the same?
Thanks for your time.