library(copula)
# declare a Gaussian copula class with a 0.7 correlation
norm.cop <- normalCopula(0.7)
# generate 500 realizations of two uniformly distributed random variables
# with the Gaussian copula dependency structure
set.seed(117)
u1 <- rCopula(500, norm.cop)
# define a t-copula class with a 0.7 correlation and 4 degrees of freedom
t.cop <- tCopula(0.7, df = 4)
# generate 500 realizations of pairs of random variables with t-copula dependence
set.seed(117)
u2 <- rCopula(500, t.cop)
# Plot the results into two graphs next to each other
par(mfcol = c(1, 2))
plot(u1, main = 'Random Variable Pairs Generated by
Gaussian Copula')
plot(u2, main = 'Random Variable Pairs Generated by
t-Copula')
fit.ml <- fitCopula(norm.cop, u1, method = "ml")
#fit.ml <- fitCopula(t.cop, u1, method = "ml")
fit.ml
