\name{copFamilies} \title{Specific Archimedean Copula Families ("acopula" Objects)} \concept{Copula Family} \alias{acopula-families} \alias{copAMH} \alias{copClayton} \alias{copFrank} \alias{copGumbel} \alias{copJoe} \docType{data} \description{ Specific Archimedean families (\code{"\linkS4class{acopula}"} objects) implemented in the package \pkg{nacopula}. } \usage{ copAMH copClayton copFrank copGumbel copJoe } \value{ A \code{"\linkS4class{acopula}"} object. } \details{ All these are objects of the formal class \code{"\linkS4class{acopula}"}. \describe{ \item{\code{copAMH}:}{Archimedean family of Ali-Mikhail-Haq with parametric generator \deqn{\psi(t)=(1-\vartheta)/(\exp(t)-\vartheta),\ t\in[0,\infty], }{psi(t)=(1-theta)/(exp(t)-theta), t in [0,Inf],} with \eqn{\vartheta\in[0,1)}{theta in [0,1)}. The range of admissible Kendall's tau is [0,1/3). Note that the lower and upper tail-dependence coefficients are both zero, i.e., this copula family does not allow for tail dependence.} \item{\code{copClayton}:}{Archimedean family of Clayton with parametric generator \deqn{\psi(t)=(1+t)^{-1/\vartheta},\ t\in[0,\infty],}{ psi(t)=(1+t)^{-1/theta}, t in [0,Inf],} with \eqn{\vartheta\in(0,\infty)}{theta in (0,Inf)}. The range of admissible Kendall's tau, as well as that of the lower tail-dependence coefficient, is (0,1). Note that this copula does not allow for upper tail dependence.} \item{\code{copFrank}:}{Archimedean family of Frank with parametric generator \deqn{-\log(1-(1-e^{-\vartheta})\exp(-t))/\vartheta,\ t\in[0,\infty]}{ -log(1-(1-e^{-theta})exp(-t))/theta, t in [0,Inf],} with \eqn{\vartheta\in(0,\infty)}{theta in (0,Inf)}. The range of admissible Kendall's tau is (0,1). Note that this copula family does not allow for tail dependence.} \item{\code{copGumbel}:}{Archimedean family of Gumbel with parametric generator \deqn{\exp(-t^{1/\vartheta}),\ t\in[0,\infty]}{ exp(-t^{1/theta}), t in [0,Inf],} with \eqn{\vartheta\in[1,\infty)}{theta in [1,Inf)}. The range of admissible Kendall's tau, as well as that of the upper tail-dependence coefficient, is [0,1). Note that this copula does not allow for lower tail dependence.} \item{\code{copJoe}:}{Archimedean family of Joe with parametric generator \deqn{1-(1-\exp(-t))^{1/\vartheta},\ t\in[0,\infty]}{ 1-(1-exp(-t))^{1/theta}, t in [0,Inf],} with \eqn{\vartheta\in[1,\infty)}{theta in [1,Inf)}. The range of admissible Kendall's tau, as well as that of the upper tail-dependence coefficient, is [0,1). Note that this copula does not allow for lower tail dependence.} } Note that staying within one of these Archimedean families, all of them can be nested if two (generic) generator parameters \eqn{\vartheta_0}{theta0}, \eqn{\vartheta_1}{theta1} satisfy \eqn{\vartheta_0\le\vartheta_1}. } \seealso{ The class definition, \code{"\linkS4class{acopula}"}. \cr \code{\link{getAcop}} accesses these families \dQuote{programmatically}. } \author{Marius Hofert} \references{ Hofert, M. (2010a), Efficiently sampling nested Archimedean copulas, \emph{Computational Statistics & Data Analysis}, in press. Hofert, M. (2010b), \emph{Sampling Nested Archimedean Copulas with Applications to CDO Pricing}, Suedwestdeutscher Verlag fuer Hochschulschriften AG & Co. KG. } \examples{ ## Print a copAMH object and its structure copAMH str(copAMH) ## Show admissible parameters for a Clayton copula copClayton@paraInterval ## Generate random variates from a Log(p) distribution via V0 of Frank p <- 1/2 copFrank@V0(100, -log(1-p)) ## Plot the upper tail-dependence coefficient as a function in the ## parameter for Gumbel's family curve(copGumbel@lambdaU(x), xlim = c(1, 10), ylim = c(0,1), col = 4) ## Plot Kendall's tau as a function in the parameter for Joe's family curve(copJoe@tau(x), xlim = c(1, 10), ylim = c(0,1), col = 4) ## ------- Plot psi() and tau() - and properties of all families ---- ## The copula families currently provided: (famNms <- ls("package:nacopula", patt="^cop[A-Z]")) op <- par(mfrow=c(length(famNms),2), mar = .6+c(2,1.4,1,1), mgp = c(1.1, 0.4,0)) for(nm in famNms) { Cf <- get(nm) thet <- Cf@tauInv(0.3) curve(Cf@psi(x, theta = thet), 0, 5, xlab=expression(x), ylab="", ylim=0:1, main = substitute(list(NAM *" "* psi(x, theta == TH), tau == 0.3), list(NAM=Cf@name, TH=thet))) I <- Cf@paraInterval Iu <- pmin(10, I[2]) ## if(I[2] == Inf) { Iu <- 1000; log <- "x" } else { Iu <- I[2]; log <- "" } curve(Cf@tau(x), I[1], Iu, xlab = substitute(theta \%in\% II, list(II = format(I))), ylab="", main=substitute(NAM * " " * tau(theta), list(NAM=Cf@name))) } par(op) ## Construct a bivariate Clayton copula with parameter theta theta <- 2 C2 <- onacopula("Clayton", C(theta, 1:2)) C2@copula # is an "acopula" with specific parameter theta curve(C2@copula@psi(x, C2@copula@theta), main = quote("Generator" ~~ psi ~~ " of Clayton A.copula"), xlab = quote(theta1), ylab = quote(psi(theta1)), xlim = c(0,5), ylim = c(0,1), col = 4) ## What is the corresponding Kendall's tau? C2@copula@tau(theta) # 0.5 ## What are the corresponding tail-dependence coefficients? C2@copula@lambdaL(theta) C2@copula@lambdaU(theta) ## Generate n pairs of random variates from this copula U <- rnacopula(n = 1000, C2) ## and plot the generated pairs of random variates plot(U, asp=1, main = "n = 1000 from Clayton(theta = 2)") } \keyword{datasets}