Hi!
We are wondering why we get a negative resid(1)^2 coefficient in Garch variance equation. If anyone could shed some light on this, we would be most thankful. Please see the attachment.
Negative resid(1)^2 coefficient in Garch variance equation
Moderators: EViews Gareth, EViews Moderator

 Posts: 4
 Joined: Fri Apr 01, 2016 4:28 am
Negative resid(1)^2 coefficient in Garch variance equation
 Attachments

 Garch.PNG (23.64 KiB) Viewed 8985 times
Re: Negative resid(1)^2 coefficient in Garch variance equat
Your output reads: "Failure to improve likelihood (nonzero gradients) after 26 iterations". In other words, you are experiencing a convergence problem. It also seems you have a very weak GARCH effect in the residuals. Either there is really no significant GARCH effect, or the mean equation is not stationary.

 Posts: 4
 Joined: Fri Apr 01, 2016 4:28 am
Re: Negative resid(1)^2 coefficient in Garch variance equat
We thank you kindly for the reply.
We have done unit root tests of data series, as well as ADF and KPSS test. The result from ADF showed that data series was stationary, while KPSS test showed that it was not stationary. We also found that it was no ARCH effect in the model. See picture. We are also wondering how we find out if the GARCH effect is significant?
Kind regards
Eager Norwegian students
We have done unit root tests of data series, as well as ADF and KPSS test. The result from ADF showed that data series was stationary, while KPSS test showed that it was not stationary. We also found that it was no ARCH effect in the model. See picture. We are also wondering how we find out if the GARCH effect is significant?
Kind regards
Eager Norwegian students
 Attachments

 ARCH test.PNG (12.77 KiB) Viewed 8938 times
Re: Negative resid(1)^2 coefficient in Garch variance equat
Try modeling your variables in first differences (i.e. d() or dlog()). Check the correlogram of residuals to see if there is any serial correlation left in your mean equation. If there is not, you are good to go. Otherwise, you need to add AR() and/or MA() lags to capture that effect. And keep in mind that there may not be any significant (G)ARCH behavior in the residuals to begin with. Sum of ARCH and GARCH coefficients close to (but smaller than) 1 indicates a strong effect.

 Posts: 4
 Joined: Fri Apr 01, 2016 4:28 am
Re: Negative resid(1)^2 coefficient in Garch variance equat
Thanks again.
Our variables are in first differences. Our depended variable is (Spot [t+1]Spot[t])/Spot[t], and our independent variable is (F[t] Spot[t])/Spot[t]. We have checked the correlogram of the residuals and there is no serial correlation, see the attachment. So that means there is no significant GARCH effect?
Kind regards
Eager Norwegian students
Our variables are in first differences. Our depended variable is (Spot [t+1]Spot[t])/Spot[t], and our independent variable is (F[t] Spot[t])/Spot[t]. We have checked the correlogram of the residuals and there is no serial correlation, see the attachment. So that means there is no significant GARCH effect?
Kind regards
Eager Norwegian students
 Attachments

 Correlogram.PNG (32.19 KiB) Viewed 8924 times
Re: Negative resid(1)^2 coefficient in Garch variance equat
Qstats (and the related pvalues) disagree with you. There are some significant lags indicating a possible serial correlation. As for the GARCH effect, you should look at the correlogram of squared residuals and then formally test for heteroscedasticity. It is hard to detect the source of the problem (if any) without seeing the actual data.
Re: Negative resid(1)^2 coefficient in Garch variance equation
Dear Eviews,
I am using the dependent variable: LN(Returns) and independent variables: Standardized Announcements.
My time series range from 20002017 thus volatility clustering is observed.
When first performing an OLS it became clear that there was hetereoscedasticity.
Ultimately performing a GARCH(1,1) equation.
My squared residuals are oke (all p values >0.05)
My correlogram of Q statistics of standardized errors certainly not (only 1st and 2nd >0.05 because of two lags of returns).
What does this mean?
When adding another ARCH effect GARCH (2,1) then the coefficient is negative but significant.
How can this coefficient be negative?
Greetings,
Jake
I am using the dependent variable: LN(Returns) and independent variables: Standardized Announcements.
My time series range from 20002017 thus volatility clustering is observed.
When first performing an OLS it became clear that there was hetereoscedasticity.
Ultimately performing a GARCH(1,1) equation.
My squared residuals are oke (all p values >0.05)
My correlogram of Q statistics of standardized errors certainly not (only 1st and 2nd >0.05 because of two lags of returns).
What does this mean?
When adding another ARCH effect GARCH (2,1) then the coefficient is negative but significant.
How can this coefficient be negative?
Greetings,
Jake
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