library("rugarch")
library("VarianceGamma")
VG.fit=vgFit(dmbp[,1], hessian = T)
summary(VG.fit)

*Data:
Parameter estimates:
       vgC         sigma        theta         nu
    0.014664     0.462267    -0.031437     0.993245
  ( 0.007915)  ( 0.011207)  ( 0.078276)  ( 0.078808)
Likelihood:         -1139.495
Method:             Nelder-Mead
Convergence code:   0
Iterations:         273*


fixed.p <- list( mu = 0.03143734,
                 shape = 0.99324513,
                 skew  =0.01466383)

spec = ugarchspec(distribution.model ="nig",fixed.pars = fixed.p)
fit = ugarchfit(data = dmbp[,1], spec = spec)