[R-SIG-Finance] rugarch and copulas
ludovic.theate at gmail.com
Fri Nov 9 11:29:38 CET 2012
Things are often clearer in the morning, and I think I changed a little bit
my mind about what I said yesterday. I really do not like the fgarch
package, since there is no well defined link between the fit and the
simulation methods. I clearly prefer the architecture spec -> fit -> sim you
use in rugarch.
So I decided to give another try, and I just would like to ask to you (and
of course the other people following this list) if you could approve/amend
the following approach :
1) I filter the data using an univariate rugarch model for each currency I
2) I evaluate the standardized residuals using “residuals(fit)/sigma(fit)”
as explained in a previous threat.
3) I transform them into uniform using either the ranks, or using a
parametric approach you described in the thread I was talking about.
Unfortunately, I am not sure I understand your explanation (“extracting any
conditional higher moment parameters from the GARCH fitted object and pass
those with the standardized residuals to the "pdist" function of rugarch
indicating the distribution used”). Could you explain with a little more
details please ?
4) With this matrix of uniform marginals, I am able to fit the copulas I am
interested in (even nested, or vine ones), compare them, make tests, and
choose the most appropriate one.
5) Using this copula, I can generate a random n_currencies x n_simulations x
n_horizon matrix containing the innovations.
6) Then, if I’ve correctly understood, I can simply plug each row (for each
currency) of this matrix inside the “ custom.dist = list(name = NA, distfit
= NA) “ option of ugarchsim and simulate the trajectories. Is it correct ?
If I made this for each currencies, I will get correlated sample
Do you think this kind of algorithm is correct ? Once again, thanks for your
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