Timeline for Issues Manually Implementing ARMA GARCH
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 30, 2021 at 9:39 | comment | added | Richard Hardy | @Jonas_Dim, check the last comment under the main post. | |
| Apr 30, 2021 at 9:32 | comment | added | Count | @CBBAM I suggest that you first try to fit a pure GARCH-model and then extend your results to the ARMA-GARCH case. Also, why do you set the constrain $\alpha+\beta=1$ ? In this case you have an IGARCH(1,1)-model, i.e. the unconditional variance does not exist. | |
| Apr 29, 2021 at 19:57 | comment | added | CBBAM | It turns out both the mean and variance of the NIG distribution are functions of other parameters, so I have changed that in my code. I have also set $\sigma_0^2:=\hat\sigma^2$ as my initial variance. However not much has changed in that I am still getting unreasonable forecast values and rather large variances. I compared my ARMA results with an existing package and visually there is not that much of a difference, so I assume the problem is in either GARCH or the distribution optimization, but I cannot tell which. | |
| Apr 29, 2021 at 19:42 | history | answered | Richard Hardy | CC BY-SA 4.0 |