data(ftse100);
data(smi);
TS1 <- cut(ftse100, "1990-11-08", "2004-03-25");
TS1Augment <- alignDailySeries(TS1, method="before");
TS2Augment <- alignDailySeries(smi, method="before");
INDEXES.RAW <- merge(TS1Augment,TS2Augment);
#Cleanup:
rm(TS1, TS1Augment, TS2Augment);
INDEXES <- mk.returns(INDEXES.RAW);
PARTIALINDEXES <- cut(INDEXES, "1993-12-31", "2003-12-31");
#Now create a data matrix from the just-created timeSeries
data <- seriesData(PARTIALINDEXES);
#Keep only the data items which are non-zero for both smi and ftse100
data <- data[data[,1]!=0 & data[,2] !=0,];
# Construct pseudo copula data. The 2nd parameter is MARGIN=2
#when applying to columns and 1 applied to rows. Hence this says to
#apply the 'edf()' empirical distribtion function() to the columns
#of the data.
Udata <- apply(data,2,edf,adjust=1);
mod.gauss <- fit.gausscopula(Udata);