because you need to have a well behaved variance... So the sum of ARCH and GARCH

coefficients should be less than unity....it seems you are satisfying it apparently...could

perform a WALD test to check the restriction. Regarding the diagnostic test results: ARCH LM

tests check the existence of variance clustering which is expected to become insignificant if

the variance had been modeled properly. The Jarque Bera tests the normality of the error

term(it is the most violated assumption and thus disregarded). Finally Ljung Q tests the

existence of autocorrelation in the model, this is also expected to turn out to be insignificant if

your mean equation happens to be proper. The selection of lag for Ljung Q is kind of ad hoc

and depend on prior information. Hope that helps.

SBC while you can also chose the model with maximum significant lags of both MEAN and

Variance equations. Degree of freedom usually used is 5 PERCENT as it is standard

criterion. chose EGARCH model if you are willing to capture as well as there exist any

threshold in your series other wise simple GARCH is suitable.

Monis Syed commented about the following EGARCH model > C(4) depicts the leverage

effect. Basically, C(4) is a coefficient of asymmetry, its positive value means positive shocks

are more responsible for conditional variance as compare to negative developments, whereas

negative sign manifests that adverse shocks/developments are responsible more for the

generation of risk in a particular time series. Here as we can see that C(4) has a negative

value; this implies negative news/shocks/developments are more relevant for risk generation

and this might be referred as a leverage effect.

and plotted it as below. Now is there any clustering volatility in this residual (one of

the conditions) so that I can employ ARCH-GARCH model?

Muhammad Anees commented> I think Yes

Dhivya Keerthiga commented> I hope yes there is clustering volatility....

Sayed Hossain commented> This plotted residual derived from the mean equation. When

small level of fluctuation follows another small level fluctuation for prolonged period or when

high level of fluctuation follows another high level of fluctuation for prolonged period, then we

call it clustering volatility. We can see this type of clustering volatility in the above figure so we

can run ARCH-GARCH model. It is a graphical representation. Now we need run ARCH test

for matematical conformation. If there is ARCH effect it means we have fulfilled the conditions

to run ARCH and GARCH model.

Meo School of Research

Shishir Shakya

Noman Arshed

Hossain Academy Note

Ade Kutu

Afolabi Luqman

Abdullah Sonnet

Asad Zaman

Atiq Rehman

Burcu Özcan

Ghumro Niaz Hussain

Muhammad Anees

Mohammad Zhafran

Muzammil Bhatti

Monis Syed

Mine PD

Moulana N. Cholovik

Muili Adebayo Hamid

Nicat Gasim

Najid Iqbal

Nasiru Inuwa

Noman Arshed

Rapelanoro Nady

Saeed Aas Khan Meo

Seye Olasehinde-Williams

Suborno Aditya

Sayed Hossain

Shishir Sakya

Sheikh Muzammil Naseer

Tella Oluwatoba Ibrahim

Younes Azzouz