Multivariate GARCH
Multivariate GARCH
Dear Tom,
I am having issues with convergence. I have attached the model as well a sample of the data. The data consists of min-by-min stock returns on AXP (American Express) and 10 mood indicators that have been transformed to eliminate the zero observations. The mood indicators were extracted with an algorithm through tweet data and are matched with the stock return data. I would like to investigate which indicator has the largest effect on the stock return volatility but I have not been successful in obtaining convergence.
Thank you,
Pieter
I am having issues with convergence. I have attached the model as well a sample of the data. The data consists of min-by-min stock returns on AXP (American Express) and 10 mood indicators that have been transformed to eliminate the zero observations. The mood indicators were extracted with an algorithm through tweet data and are matched with the stock return data. I would like to investigate which indicator has the largest effect on the stock return volatility but I have not been successful in obtaining convergence.
Thank you,
Pieter
- Attachments
-
- axp.txt
- AXP sample
- (191.48 KiB) Downloaded 933 times
-
- MV GARCH moods.RPF
- GARCH program
- (877 Bytes) Downloaded 918 times
Re: Multivariate GARCH
Why are you including those as dependent variables? Wouldn't a univariate GARCH with the "mood" variables as variance shifts be more appropriate?
Re: Multivariate GARCH
Tom,
I modeled the GARCH with the "mood" variables as variance shifts per your suggestion and that worked splendidly. I am hoping you could point me towards a procedure that would allow for out-of-sample forecasting and test the "strength" of those forecasts with another sample.
Best,
Pieter de Jong
I modeled the GARCH with the "mood" variables as variance shifts per your suggestion and that worked splendidly. I am hoping you could point me towards a procedure that would allow for out-of-sample forecasting and test the "strength" of those forecasts with another sample.
Best,
Pieter de Jong
Re: Multivariate GARCH
This would produce forecasts of the variance (which, BTW, isn't much different from forecasting a simple univariate GARCH model). How are you planning to evaluate how that works?paldejong wrote:Tom,
I modeled the GARCH with the "mood" variables as variance shifts per your suggestion and that worked splendidly. I am hoping you could point me towards a procedure that would allow for out-of-sample forecasting and test the "strength" of those forecasts with another sample.
Best,
Pieter de Jong
Re: Multivariate GARCH
I have an additional question about the shift variables. Do I have to code them as dummies (i.e., "angry" tweets per minute is a 1; zero otherwise) or could I leave them in the transformed form, which will be an aggregate of all the angry tweets in that one minute? So far, I have only seen literature describing shifts as dummy variables.
Thank you,
Pieter
Thank you,
Pieter
Re: Multivariate GARCH
No. They can be continuous. Obviously, you would need to decide whether (for instance) 20 should have twice the effect of 10 or whether you need to do some transformation of the continuous shift.
Re: Multivariate GARCH
Thank you, I opted for a transformation but I am confused about the interpretation of the parameter signs. Would a statistically significant positive sign on a "happiness" coefficient imply an expansion of the conditional volatility, i.e., tweets about a particular stock with predominantly "happy" words would add to the volatility??
Best,
Pieter
Best,
Pieter
Re: Multivariate GARCH
It would. Have you allowed for the possibility that those have an effect on the mean as well?