Generalized hyperbolic distribution (GHD) and its special cases, namely the
hyperbolic (HYP) and normal inverse Gaussian (NIG) distributions, as well as the
generalized lambda distribution (GLD)
data(smi.stocks)
> fitted.returns.mv <- fit.NIGmv(data=smi.stocks[1:500,c("CS","Nestle","Novartis")],
+ silent=TRUE)
There were 50 or more warnings (use warnings() to see the first 50)
>
> pairs(fitted.returns.mv, cex=0.5, nbins=20)
> fitted.smi.returns <- fit.hypuv(data=smi.stocks[,c("SMI")],silent=TRUE)
There were 50 or more warnings (use warnings() to see the first 50)
> par(mfrow=c(1,3))
>
> hist(fitted.smi.returns,ghyp.col="blue",legend.cex=0.7)
> hist(fitted.smi.returns,log.hist=T,nclass=30,plot.legend=F,ghyp.col="blue")
>
> qqghyp(fitted.smi.returns,plot.legend=T,legend.cex=0.7)
data(smi.stocks)
> fitted.returns.mv <- fit.NIGmv(data=smi.stocks[1:500,c("CS","Nestle","Novartis")],
+ silent=TRUE)
There were 50 or more warnings (use warnings() to see the first 50)
>
> pairs(fitted.returns.mv, cex=0.5, nbins=20)
> fitted.smi.returns <- fit.hypuv(data=smi.stocks[,c("SMI")],silent=TRUE)
There were 50 or more warnings (use warnings() to see the first 50)
> par(mfrow=c(1,3))
>
> hist(fitted.smi.returns,ghyp.col="blue",legend.cex=0.7)
> hist(fitted.smi.returns,log.hist=T,nclass=30,plot.legend=F,ghyp.col="blue")
>
> qqghyp(fitted.smi.returns,plot.legend=T,legend.cex=0.7)
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