In order to investigate Volatility models by ARCH & GARCH econometrics using Eviews you need to follow below steps in Eview 6:
1. Open Eviews6.exe
2. Click on File -> Open -> Foreign Data as Workfile…
3. Open the data file “broadband_1 “ by selecting through the path C:\Program Files\SPSSInc\Statistics17\Samples\English
4. Select MARKET_1 and MARKET_2 -> OK
5. Click on Quick -> Estimate Equation…
6. Select ARCH (Auto Regressive Conditional Hetroskedasticity in methods tab.
7. Write the dependent variable first and then than the independent variable and then click OK:
8. You will see the following output:
The variance equation table shows that MARKET_2 is not taking any part in the model, which is the property of a catalyst, and therefore the independent variable (MARKET_2) is acting as a catalyst. Since the probability of MARKET_2 is equal to 0, that is P<0.05, therefore condition or catalyst is significantly present in the model.
Now coming to variance equation table, the probability of RESID(-1)^2 [ARCH Term] is equal to 0.25, that is P>0.05, therefore volatility cannot be predicted by ARCH term as its probability is insignificant. However, the probability of the [GARCH Term] GARCH(-1) is equals to 0, that is P<0.05, therefore GARCH term is significantly predicting volatility in this model.
Now we will check significance of the whole model with the help of R-Squared or Adjusted R-Squared. If there is only one independent variable in the model then R-Squared is used and if more than one independent variable, then we use Adjusted R-Squared. In this case, since we have more than one independent variable, therefore we will consider Adjusted R-Squared.
The value of Adjusted R-Squared is equal to 0.884 * 100 = 88.4%. This means that this model is 88.4% healthy.
Lastly, the output model is generated by 3 methods:
The maximum value in the above methods is 15.89, therefore the report or result which is being generated above is represented by Schwarz criterion.
Finally, we can conclude that in this model GARCH term has predicted volatility, with the condition MARKET_2 being the catalyst.
Now we can conclude that GARCH is the model which measures volatility and the econometric tool which is used to gauge volatility is known as ARCH.
At this point, we can now conclude the investigation of Volatility models by ARCH & GARCH econometrics using Eviews
Now we are clear on tfe investigation of Volatility models by ARCH & GARCH econometrics using Eviews, so we can also discuss some more complex forms of GARCH.
If multiple volatility is connected together in a linear pattern, than the model used to measure volatility is known as Liner GARCH. Whereas, if there are multiple volatility connected together such that it forms an exponential pattern, then the model used to measure volatility is known as Exponential GARCH or e-GARCH. If the trend of the volatility changes in a way like bubble where t -> 0, then this is called Component GARCH.
Simple GARCH model is expressed as:
Whereas, E-GARCH is expressed as:
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I guess if I was a computer expert I would understand this, but I had to look just out of curiousity. Is this the same if you are working on a Linux based system? Why anyway, would I need to do it? I am curious and sure this has relevance somehow.