Timeline for Reducing possible models count for calibration in ARFIMA-GARCH models
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 7 at 14:52 | comment | added | Dmitriy | Thank you very much! | |
| Jun 6 at 10:38 | comment | added | Pleb | It says under the ARFIMAX section that they use a joint estimation scheme for the mean and variance equation. Hence, there will be variability in the ARMA parameters for each different ARMA-GARCH models you estimate. Hope this helps :-) | |
| Jun 5 at 15:07 | comment | added | Dmitriy | I would be very grateful if you did this! | |
| Jun 5 at 15:04 | comment | added | Pleb | This depends on how the rugarch package estimates the composite model. If it estimates sequentially: first the ARMA part and then the GARCH part, then the answer would be yes. However, I would read the documentation to get an understanding on how they actually estimate the composite model. :-) | |
| Jun 5 at 12:12 | comment | added | Dmitriy | Sorry, Pleb, one additional question I want you to explain me: imagine I've calibrated ARMA-GARCH model (in rugarch package it's a vanilla ARMA-sGARCH model with normal distribution). So, can I say, that all ARMA-GARCH model with different distributions or *GARCH part with the same P, Q parameters (for GARCH part) - for example, eGARCH, gjrGARCH and other - will also have the same significance for AR/MA coefficients or not? | |
| Jun 4 at 10:32 | comment | added | Dmitriy | thank you much. It's so sad that no "fast" algorithm for P,Q parameters selection in GARCH part (like stepwise algorithm for ARIMA in this article Hyndman, RJ and Khandakar, Y (2008) "Automatic time series forecasting: The forecast package for R"). | |
| Jun 4 at 10:26 | vote | accept | Dmitriy | ||
| May 7 at 12:02 | history | answered | Pleb | CC BY-SA 4.0 |