Hi,

I have just installed the latest version of fGarch together with all other Rmetrics packages (i.e. 270.73, revision 2835) and I found a couple of possible bugs in the fGarch package:

1. In the summary function, N should be calculated as length(object@data$Data) instead of length(object@data$x) because data$x does not exist (and thus normalised log likelihood is infinity).

2. In plot types 10 and 11 of the plot function, it would be better to calculate standardised residuals as sres = as.numeric(residuals(x, standardize = TRUE)) and sres2 = as.numeric(residuals(x, standardize = TRUE))^2, respectively, since the acf function is not able to handle zoo objects while fGarch is and the residuals function assigns the same class to residuals as has the modeled data.

3. In the summary function, 'object@' is missing in the command to print mean and variance equations.

4. There seems to be a problem in the computation of the hessian. The default hessian="fda" used with algorithm="nlminb" produced a covariance matrix with negative numbers on the diagonal. First, I thought this inaccuracy is the price to pay for faster computation. The setting hessian="cda" produced a covariance matrix with only positive numbers on the diagonal but it took longer to calculate the hessian. However, when I used algorithm="sqp" both hessian="fda" and hessian="cda" produced covariance matrices with negative numbers on the diagonal. Therefore, I would like to ask if such a result is OK or if there is a bug in the fortran implementation of hessian computation.

Thank you for your answer.

Best regards

Michal Miklovic




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