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When to force intercept to be zero in GARCH model?
If you force the intercept to be zero AND the sum of ARCH and GARCH coefficients equals one, then you end up with an EWMA estimator of the conditional variance, and the conditional variance is a random walk (which again might be undesirable).
Can a highly significant intercept be removed from a model?
So, a highly significant intercept in your model is generally not a problem. By the same token, if the intercept is not significant you usually would not want to remove it from the model because by doing this you are creating a model that says that the response function must be zero when the predictors are all zero.
Is the intercept always significantly different from zero?
An intercept is almost always part of the model and is almost always significantly different from zero. Note that the test of the intercept in the procedure output tests whether this parameter is equal to zero.
How to interpret EGARCH model coefficients output?
Join ResearchGate to ask questions, get input, and advance your work. In EGARCH in Mean model, if the coeffient of conditional volatity is positive and significant, it does imply that there is positive and significant relationship between say stock market returns and conditional volatility. If the asymmetric term is
Is the intercept of the conditional mean model significant?
You may not care that much about the significance of mu; it is the intercept of the conditional mean model, and there are reasons (not specific to GARCH modelling) for keeping the intercept in even though it is not significant.
Is the nullity of GARCH a good basis for selection?
There might be some relevant information in Francq & Zakoian “Testing the nullity of GARCH coefficients: correction of the standard tests and relative efficiency comparisons” (2009), but I am not entirely sure. Also note that statistical significance need not be a good basis for variable selection.
When is it OK to remove the intercept in a linear regression model?
There are reasons for keeping the intercept (like mu here) in conditional mean models even if it is not statistically significant. This has been discussed in several existing threads, e.g. “When is it ok to remove the intercept in a linear regression model?”.