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GARCH model with exogenous regressors not functioning? #160
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Unfortunately, I think the arch package seems to support only exogenous variables in calculating the mean, not in estimating the variance. I don't think the package supports what you are looking for, which is a regression for variance as a function of previous returns, previous variance, and this additional dummy value. |
ARCH contributors, is the comment from @dimab0 true? I'm trying to fit the following model: |
What @dimab0 said is correct - there is not support for exogenous variance regressors in the current version. |
@bashtage, thanks for the clarification. Do you know of a Python or R package that does support that functionality? |
I don't know much R package availability, and as far as I know there are
none for Python. pyflux would be the other place to look.
…On Tue, Feb 21, 2017 at 12:17 PM Ken ***@***.***> wrote:
@bashtage <https://github.com/bashtage>, thanks for the clarification. Do
you know of a Python or R package that does support that functionality?
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Interesting. The professor for the class recommended that we use EViews for class assignments; I was hoping to build my skill set on more widely used platforms (python/R). Is there a reason why exogenous variance regressors are not currently supported? Is this a tool that's not heavily used in the GARCH/financial econometrics space? |
In no particular order:
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Understood. I'm happy to submit a pull request. I'm just trying to understand the area a little better. Thanks for the quick replies! |
PRs welcome 👍 |
Is there any Makov-Switching GARCH out there on Python? |
It has not been coded in this toolbox and I haven't come across it. |
Is it still true that there is no support for exogenous variance regressors in this package? |
I have not added exogenous regressors for volatility. |
@bashtage do you came across any Vuong test on Python yet? |
Dear all, I am trying to use an ARX model (with exogenous variables) but I don't know how to pass the exogenous variables to the forecast method. I have tried adding a parameter x or exogp with the numpy array of the regressors to the following: But I get the error that x or exgop don't exist. How can I get this problem solved? The model is difined as follows: params = {'lags': [11,14,20,22,28,30,32,37,47], 'volatility': HARCH([1]), 'dist': Normal()} And I only need exogenous regressors for the mean. Many thanks!! |
@camontanezp I think you are missing an argument to forecast. When I do the following:
it works as expected. You are missing |
Not sure if you still care, but rugarch in R does support external regressors for variance. |
Hi, I am struggling with this exact issue, and I am working on a solution. |
The design of arch makes this a bit trickier than ideal since a VolatiltiyProcess usually takes inputs from the Mean model. I think I know how to solve it in a reasonable way to make GARCH-X and EGARCH-X models. |
I go back the arch_model() function code. It needs the "x" to be a 2-dimensional array. If not, then it shows the same error. However, I do not know why "x" should be a 2-dimensional array. After I reshape "x" as a [79,1] array (because I have 79 observations), it yields the following output. If someone is familiar in this field, please give me some comments. Thank you. @bashtage
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Hi shengnan92, X can be forced to be a 2 dimensional array and still work with only one set of observations because you (the code in this case) can set the 1st array to be zeros. This is a somewhat common thing to do because it allows for generalisation in adding a constant to your model. E.g.: if you want a constant in your model, the 1st X row would be made fully of 1s. |
The issue with the exog shape has been fixed in master. Master and the next release will accept 1d exog as long as it has the right number of observations. |
Hi, I am also having problems with the exogenous model forecast using ARX function in the arch_model library. I have one exogenous continuous variable S&P500 I want to use to help predict the mean log returns of another variable using ARX mean model. I am having issues getting a multi-step forecast horizon. I have tried many fixes based upon the comments in this chain, but nothing is working. Here is a copy of the code that get me one step ahead forecasts.
I'm hoping you can tell me how to correct my code to get multi-step forecast results when I include an exogenous variable. |
There is no support for multistep forecasts with exogenous regressors, so you will need to roll your own. |
Hi @bashtage I'm too having trouble with GARCH model with exogenous regressors. Below is my code and I keep having the value error. Was wondering if you might know where I got it wrong?
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I still think exog variables haven't been implemented yet. |
Are there any other standard garch implementations in Python? Hard to believe there isn't a package in Python with support for exogenous variance variables |
Hi @kcho5820 , it's a long shot but did you manage to fix this problem? I am having the same issue at the forecasting step. |
Hi @bashtage, since exogenous regressors are still not supported in the variance step, could you elaborate a bit on what you meant by the above re a reasonable way to make GARCH-X models work using |
Hello,
I'm struggling to figure out how to properly use this package to fit a GARCH(1,1) model with an exogenous variable. Here's an example Jupyter notebook to illustrate what I'm trying to do.
In short, using the canonical example of daily S&P 500 returns, I'm trying to add a dummy variable to a GARCH(1,1) model to examine the effect of Mondays. If I fit a model like so,
arch_model(returns, x=mondays).fit().summary()
, the summary output does not include any information for the dummy variable, making me think the variable was ignored altogether.Looking through the ARCH documentation, I found a page specifying that I may need to specify a mean model for exogenous regressors. If I explicitly specify the mean model to be HARX, like so,
arch_model(returns, x=mondays, mean='HARX').fit().summary()
, I receive the following exception:ValueError: x must be nobs by n, where nobs is the same as the number of elements in y
.Am I missing something simple here? I'm trying to use this package for a graduate class in econometrics, and this question is causing some serious pain. Any help would be greatly appreciated. Thanks!
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