Thank you Alexios for always prompt and patient reply! Yes, you are right. It does not make sense to look at the simulated cov for 1-ahead. I was trying to do a portfolio allocation exercise.
1) If the mean-variance approach is adopted, basically what I need is the 1-day-ahead return of each assets and their cov. I suppose the former should come from the margin parts (e.g. ARMA(1,1)-GARCH(1,1)-ghst) given the estimated marginal parameters and the latter should come from the copula part (e.g. DCC(1,1)-t-copula) given the estimated copula parameters. In R, *what shall I do?* for example I have the following: > library(rmgarch) > data(dji30ret) > Dat = dji30ret[, 1:3, drop = FALSE] > uspec = ugarchspec(mean.model = list(armaOrder = c(1,1)), variance.model = > list(garchOrder = c(1,1), model = "sGARCH"), + distribution.model = "jsu") > spec1 = cgarchspec(uspec = multispec( replicate(3, uspec) ), VAR = FALSE, > robust = FALSE, lag = 2, lag.max = NULL, + lag.criterion = c("AIC", "HQ", "SC", "FPE"), external.regressors = NULL, + robust.control = list("gamma" = 0.25, "delta" = 0.01, "nc" = 10, "ns" = 500), + dccOrder = c(1,1), asymmetric = FALSE, distribution.model = list(copula = c("mvnorm", "mvt")[2], + method = c("Kendall", "ML")[2], time.varying = TRUE, + transformation = c("parametric", "empirical", "spd")[1])) > fit1 = cgarchfit(spec1, data = Dat, parallel = parallel, parallel.control > = parallel.control, + fit.control = list(eval.se=TRUE)) > show(fit1) *-------------------------------------------------* * Copula GARCH Fit * *-------------------------------------------------* Distribution : mvt DCC Order : 1 1 Asymmetric : FALSE No. of Parameters : 30 [VAR GARCH DCC UncQ]: [0+24+3+3] No. of Series : 3 No. of Observations : 5521 Log-Likelihood : 43676.18 Av.Log-Likelihood : 7.911 Optimal Parameters --------------------------------------------------- Estimate Std. Error t value Pr(>|t|) [AA].mu 0.000531 0.000252 2.106962 0.035121 [AA].ar1 -0.582146 0.075357 -7.725139 0.000000 [AA].ma1 0.638169 0.069877 9.132792 0.000000 [AA].omega 0.000003 0.000001 2.046048 0.040752 [AA].alpha1 0.045806 0.008897 5.148264 0.000000 [AA].beta1 0.948470 0.012120 78.255127 0.000000 [AA].skew 0.141182 0.069022 2.045450 0.040810 [AA].shape 1.902134 0.111488 17.061336 0.000000 [AXP].mu 0.000627 0.000168 3.732932 0.000189 [AXP].ar1 0.730182 0.072262 10.104678 0.000000 [AXP].ma1 -0.784923 0.065556 -11.973334 0.000000 [AXP].omega 0.000001 0.000001 1.893198 0.058332 [AXP].alpha1 0.058253 0.012840 4.536872 0.000006 [AXP].beta1 0.940747 0.014063 66.893758 0.000000 [AXP].skew 0.131492 0.048365 2.718777 0.006552 [AXP].shape 1.868931 0.097636 19.141771 0.000000 [BA].mu 0.000586 0.001033 0.567440 0.570415 [BA].ar1 -0.385522 5.202037 -0.074110 0.940923 [BA].ma1 0.394759 5.336133 0.073978 0.941028 [BA].omega 0.000004 0.000005 0.729471 0.465714 [BA].alpha1 0.046706 0.056782 0.822551 0.410764 [BA].beta1 0.942283 0.066474 14.175183 0.000000 [BA].skew 0.072497 0.522772 0.138678 0.889705 [BA].shape 1.710008 0.085270 20.054081 0.000000 [Joint]dcca1 0.008234 0.001330 6.190368 0.000000 [Joint]dccb1 0.989416 0.002026 488.351900 0.000000 [Joint]mshape 13.574529 1.569377 8.649627 0.000000 Information Criteria --------------------- Akaike -15.812 Bayes -15.780 Shibata -15.812 Hannan-Quinn -15.801 Elapsed time : 1.85734 ps. seems I can't use ghst for GH skew-t but use jsu instead. When I use ghst in ugarchspec, the cgarchfit will report the following error message: Error in if (!any(distribution == valid.distribution)) stop("\nugarchspec-->error: the cond.distribution does not appear to be a valid choice.") : missing value where TRUE/FALSE needed 2) If the mean-CVaR approach is adopted, I suppose to have a large number (say, 10000) of 1-day-ahead return of each assets at different scenarios. I think it can be done with sim1 = cgarchsim(fit1, n.sim = 1, m.sim = 10000, startMethod = "sample"). *Am I right?* 3) for other exercises, i.e. fitting Archimedean copulas (seems like the rmgarch package only support gaussian and t copula), I need transform the standardised residuals to uniform via CDF or ecdf. I suppose the residuals is standardised (*Am I right?*) by using rugarch, again assume ARMA(1,1)-GARCG(1,1)-ghst, then *how can I do this transformation* to obtain uniform pseudo-observations to start a fitting using copula package? Btw, do you have any suggestion to do this exercise? -- View this message in context: http://r.789695.n4.nabble.com/copula-with-rmgarch-tp4616138p4620878.html Sent from the Rmetrics mailing list archive at Nabble.com. _______________________________________________ R-SIG-Finance@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.