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I'm currently working with multivariate GARCH representations of time-series for financial data using the rmgarch R package. This package in turn uses the well-known rugarch package to fit the 'marginal' GARCH processes.

Right now I'm evaluating the goodness-of-fit and parameter estimates when using different conditional distributions for the variance innovations. It seems that (skewed) student's t and skewed normal fit my data better as they present heavier-than-normal tails, skew, and excess kurtosis. In addition, the parameter estimates seem to become more significant (particularly the estimate for $\alpha_1$) when using non-normal distributions.

Typically, I would analyse the standardised residuals of my model (available in "fit object"@mfit$stdresid for a DCC-fitted model), and use for example a QQ plot against the theoretical distribution to examine the behaviour, along with an ACF plot to examine autocorrelations. This is easily done when assuming normal innovations as the standard qqnorm in R does the trick, however I'm less sure how to do this procedure for the skewed normal and (skewed) $t$ distributions.

Is there a good way to produce these kinds of QQ-plots in R? The GARCH package(s) will readily output estimates for skew and shape, but I'm not sure how to incorporate these into QQ-plots.

Also, as a side question: currently I'm fitting three time series. Two of them seem to want Student's $t$ distribution, and one skewed normal. For the joint innovations, however, I'm currently using a multivariate normal distribution. Does this make sense from a mathematical perspective? As you might tell, I'm relatively inexperienced with time-series models, but this seems akin to fitting a joint distribution normally but with non-normal marginal distributions, which seems odd.

Thanks for any help! :)

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To assess the distributional assumption of a GARCH model, you can look at the probability integral transform (PIT) of the standardized residuals. It can be obtained by pit(fit) where fit is a fitted model object from rugarch. If the model is a good approximation of the data, the PIT should be roughly Uniform[0,1]. If you want to do a QQ plot, you can use the qnorm function to go from uniform to standard normal and then use the qnorm function for the QQ plot. See p. 41-43 of the rugarch vignette for a more detailed discussion.

library(rugarch)
data(dmbp) # example data
spec=ugarchspec()
fit=ugarchfit(data=dmbp[,1], spec=spec)
pit=pit(fit) 
hist(pit) # this should be approximately Uniform[0,1]
norm=qnorm(pit)
hist(norm) # this should be approximately standard normal
qqnorm(norm); abline(a=0, b=1) # this is your QQ plot

As to your side question, multivariate normal distribution does not make sense if the marginals are not normal. A possible fix for that is to use a copula-GARCH model (available in rmgarch). It does not yield a DCC-type dynamic, though.

On the other hand, I guess it should be possible to have different distributional assumptions for the different marginals in a DCC model in rmgarch (I have by now forgotten how these things are implemented there). After all, the DCC structure is built on top of the univariate GARCH models; it takes them as the starting point. The estimation might also be carried out in two steps: first the marginals and then the DCC part. So the "multivariate normal assumption" might be used for fitting the DCC structure, and it might act as a normal copula without affecting the marginals. But I am not sure about this.

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