anozman wrote:Yes Tom. The garch effect is not strong. in fact, a single shift paramter in the variance equation is sufficient to make the standardised residuals behave well. i am just not sure whether the violation of the skewness will have an impact on my results as t and GED can deal with fat tails, but not the skewness. unfortunately, almost all financial series related models suffer from this little problem. i was wondering how other people adjust their models to deal with this problem using RATS. can LOGSKEWTDENSITY be used in this case?
anozman wrote:Thanks Tom for your advice!
Unfortunately, the ARCH effect does exist, but it can be dealt with easily. The problem is the raw residuals that have fat tails and are also skewed. This is the only reason why I have decided not to use the RATS pre-programmed garch routine but code it using the skewed t. However, it seems it is a bit difficult to achieve the convergence for some model specifications. I have been thinking about how to improve this in a systematic way using RATS. Do you have any other suggestions that can deal with the residual distributional issues in an easier way with available RATS codes (or I can modify them to achieve the goal)?
Thanks for your help!
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