[R-SIG-Finance] rcopula error

[Joe W. Byers]

This is a modified rcopula function.  The commented lines of code are 
the  only way I could get it to work.

I know there is a problem with the if statement when a covariance matrix 
is passed to this function.



#************ rcopula.gauss that works
rcopula.gauss<-function (n, Sigma = equicorr(d, rho), d = 2, rho = 0.7)
{
      d <- dim(Sigma)[1]
#    if (sum(diag(Sigma)) != d)
#        stop("Sigma should be correlation matrix")
      mnorm <- rmnorm(n, Sigma = Sigma)
      matrix(pnorm(mnorm), ncol = d)
}


Here is my example.


require(VGAM)
require(QRMlib)
require(gamlss)
require(fCopulae)
require(copula)
require(date)

df<-7.5
sig1<-.5
var2<-2.5
seed<-1234
set.seed(seed);
#base on sas code from web site
#generate t distributed data
tdata<-data.frame(date=seq(as.numeric(as.date('06/01/2001')),as.numeric(as.date('11/01/2002')),1))
tdata$tdata<-.05*tdata$date+rTF(length(tdata$date),mu=5000,sigma=sig1,nu=df)

#estimate model data~a * date + constant
      fam<-TF

y1<-gamlss(tdata~date+1,data=tdata,family=fam,na.rm=T,nu.start=16.58,sigma.formula=~1
        ,nu.formula=~1)
      y1<-gamlss(tdata~date+1,data=tdata,family=TF(mu.link =
identity,sigma.link = log, nu.link = log))
      plot(y1,na.rm=T)


   #generate normal garch distributed data
   set.seed(12345)

ndata<-data.frame(date=seq(as.numeric(as.date('06/01/2001')),as.numeric(as.date('11/01/2002')),1))
    ndata$rnd<-rnorm(length(ndata$date))
    ndata$v<-NaN
    ndata$e<-NaN
    ndata$v[1]<-.0001+.2*var22 +.75* var2
    ndata$e[1]<-sqrt(ndata$v[1])*ndata$rnd[1]
    for (i in 2:length(ndata$date) ) {
      ndata$v[i]<- 0.0001 + 0.2 * ndata$e[(i - 1)]2 + 0.75 * ndata$v[(i
- 1)]
      ndata$e[i]<-sqrt(ndata$v[i])+ndata$rnd[i]
    }
    # add constant 25
    ndata$ndata<-25+ndata$e
    # estimate garch 1,1 model of normally distributed data
    yn<-garchFit(ndata~garch(1,1),data=ndata)

   #y = rcauchy(519, loc=-4, scale=1)
   # generate cauchy data
   set.seed(seed);

cdata<-data.frame(date=seq(as.numeric(as.date('06/01/2001')),as.numeric(as.date('11/01/2002')),1))
   #cdata$cdata<-unlist(-4+tan(runif(length(cdata$date))-.5)*pi)
   cdata$cdata<-rcauchy(length(cdata$date),loc=-4,scale=1)
   #estimate cauchy model
   yc<-vglm(cdata ~ 1 , data=cdata,cauchy1(lloc="identity"), trace=TRUE,
crit="c")



   #create transformed uniform residuals for all estimated series' residuals

resids<-data.frame(date=tdata$date,tresids=y1$residuals,nresids=yn at residuals,cresids=yc at residuals)

resids$tresids<-pnorm(pTF(resids$tresids/exp(y1$sigma.coefficient),y1$nu.coefficients))
   resids$cresids<-pnorm(pcauchy(resids$cresids,yc at coefficients[1]))

resids$nresids<-pnorm(resids$nresids,mean(resids$nresids),sd=sd(resids$nresids))

yall<-merge(tdata,merge(cdata,ndata,by.x="date",by.y="date",all=T),by.x="date",by.y="date",all=T)

rnds<-data.frame(rmvnorm(2000,sigma=cov(resids[-1]),mean=mean(resids[-1])))
   names(rnds)<-c('T',"Normal",'Cauchy')
#fit a normal copula to the uniform residuals
   cop.fit<-fit.norm(resids[,2:4])
#simulate the normal copula
# cop.sim1<-rmnorm(2000, Sigma=cop.fit$Sigma, mu=cop.fit$mu)


#************Here is my problem
   cop.sim<-rcopula.gauss(2000, Sigma=cop.fit$Sigma)

#*******

   #,d=sum(diag(cop.fit$cor)))#, mu=cop.fit$mu)
#transform the simulated correlated normal rv' to uncorrelated
individual distributions
   sims<-data.frame(cop.sim)
   names(sims)<- c('T',"Normal",'Cauchy')

sims$T<-qTF(pnorm(sims$T),mu=y1$mu.coefficients[1],sigma=exp(y1$sigma.coefficients), 

nu=y1$nu.coefficients)
 
sims$Normal<-qnorm(sims$Normal,mean(resids$nresids),sd=sd(resids$nresids))
   sims$Cauchy<-qcauchy(pnorm(sims$Cauchy),location=yc at coefficients[1])

sims$T<-seq(as.numeric(as.date('11/01/2002'))+1,as.numeric(as.date('11/01/2002'))+2000,1)