+ Reply to Thread
Results 1 to 1 of 1

Thread: copula-based VaR calculation in R

  1. #1
    Points: 34, Level: 1
    Level completed: 68%, Points required for next Level: 16

    Thanked 0 Times in 0 Posts

    copula-based VaR calculation in R

    I'm working on a value-at-risk calculation using copulas on different stock market indices. I know how to fit the copula, but I can't figure out how to apply the VaR approach in the next step. The concept of copulas is relatively new to me and has proven to be very challenging for an average master student..I defined 3 periods in which I want to investigate the evolution of the VaR over time. When running the code, R returns a value for the VaR. But when running the code for another time period, R gives the same value as the previous period..Am I overlooking/forgetting something? The code I provided shows the bivariate example of china and india using the normal copula. I plan to extent it with the t and clayton copula in a further stage.

    cop_model = normalCopula(dim = 2)
    m = pobs(as.matrix(cbind(CHINA_INDIA$CHINA.LOG[571:406],CHINA_INDIA$INDIA.LOG[571:406]))) 
    fit = fitCopula(cop_model, m, method = "ml")
    tau(normalCopula(param = coef(fit)))
    cor(CHINA_INDIA$CHINA.LOG[571:406],CHINA_INDIA$INDIA.LOG[571:406], method = "kendall") #check whether correlation is more or less preserved
    u = rCopula(500, normalCopula(coef(fit), dim = 2)) #simulate some observations from the copula
    cdf = pCopula(u, normalCopula(coef(fit), dim = 2) #construct cdf of the copula
    VaR(cdf, p=0.95)
    Last edited by spunky; 04-23-2017 at 03:02 PM.

+ Reply to Thread


Tags for this Thread

Posting Permissions

  • You may not post new threads
  • You may not post replies
  • You may not post attachments
  • You may not edit your posts

Advertise on Talk Stats