Calculate the conditional quantile or CoVaR with different type of Copula and marginal distribution. In this package several bivariate copula families are included for bivariate analysis. It provides functionality of elliptical (Gaussian and Student t) as well as Archimedean (Clayton, Gumbel, Frank, Plackett, BB1, SCJ, rotated clayton and rotated Gumbel) copulas to cover a large bandwidth of possible dependence structures.

Andrea Ugolini andreaugolini@me.com \ Juan Carlos Reboredo Noguiera juancarlos.reboredo@usc.es

Reboredo, J. C., & Ugolini, A. (2016). Quantile dependence of oil price movements and stock returns. Energy Economics, 54, 33-49.

load("Data_demo.Rdata") source("CoVaR.R") source("DynCopulaCoVaR.R") source("DynCopulaCoVaRUpper.R") source("skewtdis_inv.R") require("pracma") require("copula")

CoVaR1part = CoVaR(0.05,0.05,par=par1_1,par2=par2_1,dof=tailBrazil,gamma=asyBrazil,cond.mean=meanBrasil1, cond.sigma=sigmaBrasil1,dist="tskew",type="Student")

CoVaR2part = CoVaR(0.05,0.05,par=par1_2,par2=par2_2,dof=tailBrazil,gamma=asyBrazil,cond.mean=meanBrasil2, cond.sigma=sigmaBrasil2,dist="tskew",type="Student")

CoVaR1partUp = CoVaR(0.95,0.95,par=par1_1,par2=par2_1,dof=tailBrazil,gamma=asyBrazil,cond.mean=meanBrasil1, cond.sigma=sigmaBrasil1,dist="tskew",type="StudentUp")

CoVaR2partUp = CoVaR(0.95,0.95,par=par1_2,par2=par2_2,dof=tailBrazil,gamma=asyBrazil,cond.mean=meanBrasil2, cond.sigma=sigmaBrasil2,dist="tskew",type="StudentUp")

CoVaR1D = CoVaR1part$CoVaR CoVaR2D = CoVaR2part$CoVaR CoVaR1U = CoVaR1partUp$CoVaR CoVaR2U = CoVaR2partUp$CoVaR

TimeCoVaRD = rbind(CoVaR1D,CoVaR2D) TimeCoVaRU = rbind(CoVaR1U,CoVaR2U)

plot(as.matrix(TimeCoVaRD),type="l",col="blue", ylim=c(-0.5,0.5),xlab="Time",ylab="") lines(VaR,col="black",lty=2) lines(TimeCoVaRU,col="red",lty=4) lines(VaRup,col="green",lty=3) abline(h=0,col="gray33")

R Code CoVaR with Copula