Timeline for Efficient online (rolling window) estimation of a GARCH model
Current License: CC BY-SA 4.0
17 events
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| Dec 27, 2021 at 15:00 | history | tweeted | twitter.com/StackStats/status/1475481639689940996 | ||
| Dec 26, 2021 at 21:01 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Aug 19, 2021 at 21:07 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Apr 12, 2021 at 22:03 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Mar 6, 2021 at 10:55 | history | edited | Richard Hardy |
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| Nov 8, 2020 at 18:00 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Nov 2, 2020 at 16:22 | comment | added | mlofton | Thanks Richard. I didn't realize that that was the paper you were referrring to. I'll check it out when time permits. | |
| Nov 1, 2020 at 13:40 | comment | added | mlofton | if you can link to it or give the title, it's appreciated. | |
| Oct 5, 2020 at 12:44 | answer | added | Richard Hardy | timeline score: 1 | |
| Dec 11, 2019 at 4:20 | comment | added | mlofton | This gets straight to the point of the Duncan and Horn paper. Barely any words. Just equations. ee263.stanford.edu/lectures/recursive.pdf | |
| Dec 11, 2019 at 3:56 | comment | added | mlofton | Note that what D and H do is really just a specific case of the KF so you'd kind of be re-inventing the wheel if you built their algorithm with code. I only suggest that approach because, with some twisting and turning, it might lead to a way to mimic the rolling GARCH heuristic EXACTLY. I don't think (because of initialization stuff ) it's possible to formulate the KF in such a way so that you obtain the same exact results as rolling GARCH gives. At the same time, you might not care about rolling window results in which case, stick with the KF because Duncan and Horn is then redundant. | |
| Dec 11, 2019 at 3:46 | comment | added | mlofton | This is it but it doesn't look that easy to get. I had ( or have ?) a hardcopy somewhere so I might have the pdf somewhere also. If you have trouble, let me know and I can look to see if I have the pdf somewhere. tandfonline.com/doi/abs/10.1080/01621459.1972.10481299 | |
| Dec 11, 2019 at 3:38 | comment | added | mlofton | Hi Richard: I didn't realize that it was you asking the question. Note that the one drawback is that you're not going to get the results that a rolling ugarch estimation procedure would give. Duncan and Horne is the paper for doing regresssions by using the previous value's estimate. The problem is that GARCH is different enough from regression that I don't know how hard it would be to do the Duncan and Horne thing for GARCH. If you could do that then I think you could match the results of a rolling sum procedure. Let me find the link to that paper and I'll put it in another comment. | |
| Dec 10, 2019 at 17:23 | comment | added | Richard Hardy | @mlofton, thank you! I suspected the idea of Kalman filter might be worthwhile but was not aware of any existing attempts. I will look up the reference and see if it provides anything interesting. | |
| Dec 10, 2019 at 17:22 | comment | added | mlofton | Hi: if you model garch using a state space formulation, then you have the updating equations ( the KF equations ) at your disposal which make computations convenient. I think there's a paper by harvey and ruiz on how to do that but it might be for arch. I forget exactly. If you google for "garch state space", I bet something will turn up. good luck. | |
| Dec 10, 2019 at 16:52 | comment | added | Richard Hardy |
I have glimpsed at the source code of the functionugarchroll in the rugarch package in R and it seems it just uses brute force. But I may be mistaken.
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| Dec 10, 2019 at 16:50 | history | asked | Richard Hardy | CC BY-SA 4.0 |