CopulaStudent | R Documentation |
Functions for copula density, generating random variates and fitting
dcopula.t(Udata, df, Sigma, log = FALSE)
rcopula.t(n, df, Sigma)
fit.tcopula(Udata, method = c("all", "Kendall", "Spearman"),
startdf = 5, ...)
df |
|
log |
|
method |
|
n |
|
Sigma |
|
startdf |
|
Udata |
|
... |
ellipsis, arguments are passed down to |
If in the call to fit.tcopula()
, method = "all"
, then
all parameters are estimated, i.e., the degrees of freedom and
the dispersion parameters (initial values from Spearman
correlations). In case of either method = "Kendall"
or
method = "Spearman"
, the corresponding rank correlations are
used and the optimization is only carried out with respect to the
degrees of freedom parameter. The initial value for the DF is given by
startdf
. See pages 197 and 229–236 of QRM.
A vector of density values of length n for dcopula.t()
. A
matrix of random variates for rcopula.t()
. A list object
containing parameter estimates and details of fit for function
fit.tcopula()
.
nlminb
ll <- c(0.01,0.99)
#create perspective plot for bivariate density:
BiDensPlot(func = dcopula.t, xpts = ll, ypts = ll, df = 4,
Sigma = equicorr(2, 0.5))
S <- equicorr(d = 6, rho = 0.7)
data <- rcopula.t(2000, df = 4, Sigma = S)
pairs(data)
## Fitting Student's Copula
data(smi)
data(ftse100)
s1 <- window(ftse100, "1990-11-09", "2004-03-25")
s1a <- alignDailySeries(s1)
s2a <- alignDailySeries(smi)
idx <- merge(s1a, s2a)
r <-returns(idx)
rp <- series(window(r, "1994-01-01", "2003-12-31"))
rp <- rp[(rp[, 1] != 0) & (rp[, 2] !=0), ]
Udata <- apply(rp, 2, edf, adjust = 1)
copt2 <- fit.tcopula(Udata, method = "Kendall")
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