[R-SIG-Finance] Adding external regressors on conditional variance model

[alexios]

Likely related to the non-negativity constraints, so scaling helps.
However, I suggest you try the eGARCH model instead for regressors in 
the variance, and search some previous posts regarding setting
the bounds on the coefficients.

Alexios

On 20/08/2015 18:19, Assis Duraes wrote:
> Thank you very much for your prompt answer and help, Alexios.
>
> I am running now some models and found some results that are puzzling me.
>
>  From another thread I saw one recommendation that we should "(...) pass
> values in the external regressor which are close in scale to the
> variance equation", what makes sense to me...
>
> However, I noticed that when I define the external regressors with
> values close to the return series, the coefficients calculated for the
> external regressors are non-significant, while I have a strong
> hypothesis that those should be relevant in the process. Nevertheless, I
> noticed that when scaling to an annual basis the external regressor
> coefficients becomes significant...
>
>
> Attached follow a sample code (sample_gjr_extregressor.R) and simplified
> database (data.txt) with daily returns (r) and lagged realized variance
> (lag_extreg) I am using as inputs for the models.
>
> 	
> 	
>
> There are three models as below:
>
> Model1: Standard GJR
> Model2: GJR with lagged daily realized variance
> Model3: GJR with lagged  realized variance annualized (scaled by 252)
>
> The table below summarizes the results found.
>
> 	model 	omega 	alpha1 	beta1 	gamma1 	   delta1 	p-val_omega
> p-val_alpha1 	p-val_beta1 	p-val_gamma1 	p-val_delta1 	LL 	For_01d
>
> 	Model1 	0.011449 	0.131705 	0.89924 	-0.08386 	   NA 	0.001217
> 0.000000 	0.000000 	0.000012 	0.000000 	-2033.96 	0.800619
>
> 	Model2 	0.011449 	0.131705 	0.899244 	-0.08387 	1.23E-08 	0.002944
> 0.000000 	0.000000 	0.000012 	0.999999 	-2033.96 	0.800635
>
> 	Model3 	0.004839 	0.066382 	0.69109 	-0.08387 	0.001161 	0.633308
> 0.037429 	0.000000 	0.010968 	0.000451 	-2012.57 	0.778901
>
>
> Any idea or suggestion on what might be happening?
>
> Thanks again in advance for any help with that issue.
>
> Best Regards,
> Assis.
>
>
>
> 2015-08-20 11:01 GMT-03:00 alexios <alexios at 4dscape.com
> <mailto:alexios at 4dscape.com>>:
>
>     Hi,
>
>     The answer is yes and yes. Add variable(s) lagged.
>
>     Best,
>
>     Alexios
>
>
>     On 20/08/2015 14:57, Assis Duraes wrote:
>
>         Hi,
>
>         first of all I would like to thanks for the rugarch package. it
>         is really
>         useful and a very nice package.
>
>         I am investigating the effect of external variables on
>         conditional variance
>         models forecasts. more specifically, I am would like to check if the
>         addition of implied volatility and realized variance as external
>         regressors
>         on a GJR (1,1) model somehow enhance the daily volatility
>         forecasts of it.
>
>         Looking for a tool to help modelling it I found the rugarch
>         package, and
>         started looking into it. In fact, at this point, I believe I
>         have a very
>         basic question. but did not find an answer on previous posts or
>         in the
>         package documentation.
>
>         Should I inform the external regressors matrix in model spec
>         already lagged
>         or not? I imagine, yes, since I did not find in any place where
>         specify the
>         lags for those regressors, but would like to confirm. In case
>         affirmative,
>         If I want to use a same variable with different lags I need to
>         inform it
>         multiple times, obviously with different lags, in
>         external.regressors
>         matrix, correct?
>
>         My apologies in advance if it is explained somewhere, but as I
>         explained, i
>         search without much success..
>
>         Thanks in advance for any help with that,
>         Assis.
>
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