Warning: This is the development version of the package, please use with caution!

The R package CopulaOne implements functions for bivariate copulas that must satisfy the following two properties:

• It can account for full-range tail dependence for both upper and lower tails.
• It can account for both reflection symmetry and asymmetry between upper and lower tails.

Bivariate copulas have been widely used either in modeling bivariate dependence structures or building multivariate dependence models such as Vine copulas and factor copulas. In the literature, there are numerous parametric bivariate copula families. It is often very time consuming to select copula families from many different candidate copula families. The R package CopulaOne aims at implementing a collection of very flexible bivariate copulas that are parsimonious and very flexible. The copulas implemented in CopulaOne should be able to account for most bivariate dependence patterns by a single copula, and this is also why we name the package as CopulaOne. Compared to those existing bivariate parametric copula families, the main merit of the bivariate copulas implemented here is that, they can account for full-range tail dependence in both upper and lower tails, and the upper and lower tails can be either reflection symmetric or asymmetric. The package is under active development, and the following copulas have been implemented: GGEE, PPPP. The following figures show how flexible the GGEE and PPPP copulas are. Please refer to Hua (2017) for more details about the GGEE copula, and Su and Hua (2017) for the details about the PPPP copula.

• The R package CopulaOne can be easily installed from github by the following two lines.

library(devtools)
install_github("larryleihua/CopulaOne")

• You may need to install the following first.

install.packages("hypergeo", dependencies = T)

• Naming rules: The name GGEE_COP is used for the two-parameter copula that are based on Gamma-Gamma-Exponential-Exponential mixtures. The name CopulaOne is used as a unified platform for implementing various functions that can be used as coherent as possible. For other copulas, replace GGEE by the corresponding names, such as PPPP.

• Simulation based on the copula can be done as follows:

library(CopulaOne)
UU <- rGGEE_COP(10, a=0.5, b=0.8)

• Joint density, cdf functions can be derived as

den <- dGGEE_COP(0.2, 0.3, 1.2, 0.5)
cdf <- pGGEE_COP(0.2, 0.3, 1.2, 0.5)
cat("The copula density and cdf are:", den, cdf, "\n")

• Contour plots can be plotted directly for a given copula as follows.

layout(matrix(c(1,2),1,2))
plotCopulaOne(c(1.2, 0.5), marg = "normal", copula_family = "GGEE")
plotCopulaOne(c(1.2, 0.5), marg = "uniform", copula_family = "GGEE")

• Kendall's tau and Spearman's rho of the GGEE copula can be evaluated by

tauGGEE_COP(a=0.7, b=0.4)
sprGGEE_COP(a=0.7, b=0.4)

• Upper extreme value copula of the GGEE copula can also be evaluated with the following joint cdf and pdf functions. Lower extreme value copula should be the same if one exchanges a and b.

pUEV_GGEE_COP(0.3, 0.4, b=1.2)
dUEV_GGEE_COP(0.3, 0.4, b=1.2)

• An example of fitting dependence between exchange rates returns by the GGEE copula [Warning: this step can be slow on your computer!]

data("euro0306")
dat <- uscore(euro0306[,c(2,3)])[1:50,]
par <- c(0.3, 0.3)
fit <- fitCopulaOne(par, dat=dat, copula_family = "GGEE")

• An example of fitting dependence between exchange rates returns by the PPPP copula

data("euro0306")
dat <- uscore(euro0306[,c(2,3)])[1:50,]
par0 <- c(0.3,0.3,1,1)
patternpar <- c(1,2,0,0)
fit1 <- fitCopulaOne(par0, patternpar=patternpar, dat=dat, se=F, copula_family = "PPPP")