QRM: CopulaStudent – R documentation

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QRM

CopulaStudent

Student's t Copula

Description

Functions for copula density, generating random variates and fitting

Usage

dcopula.t(Udata, df, Sigma, log = FALSE)
rcopula.t(n, df, Sigma)
fit.tcopula(Udata, method = c("all", "Kendall", "Spearman"),
            startdf = 5, ...)

Arguments

`df`	`numeric`, degrees of freedom.
`log`	`logical`, whether log density values should be returned.
`method`	`character`, method for fitting.
`n`	`integer`, count of random variates
`Sigma`	`matrix`, correlation matrix
`startdf`	`numeric`, initial DF value.
`Udata`	`matrix`, dimension n \times d, where d is the dimension of the copula and n is the number of pseudo-uniform values.
`...`	ellipsis, arguments are passed down to `nlminb()`.

Details

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.

Value

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().

Examples

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")

QRM

Provides R-Language Code to Examine Quantitative Risk Management Concepts

v0.4-31

GPL (>= 2)

Authors

Bernhard Pfaff [aut, cre], Marius Hofert [ctb], Alexander McNeil [aut] (S-Plus original (QRMlib)), Scott Ulmann [trl] (First R port as package QRMlib)

Initial release

2020-02-15