 
Hi All,
I need your help.
I have 9 stock returns(y) to analyze. I am running an regression : y on lagged values of Y and X1&X2 (exogenous variables). If I run an ols  regression then  LM test etc on the residuals shows existence of GARCH  effect.(although there are serial correlation present in the residuals  too but they are mild i.e. significant at 10% level )
Therefore I proceed to model the volatility using an appropriate GARCH model. Going by the method suggested by Walter Enders calculate RSS’, AIC’ , BIC’ etc. I restricted my search in 6 models ....from  GARCH(1,1) to GARCH(2,2) only. Suppose these exercises is suggesting  me a GARCH(1,1) or EGARCH(1,1) model. But after I fit the model and collect the residuals and subject  them  to tests, I observe: though there are no GARCH effect left (LB stat is giving p-values as 0.9999 for squared residuals ) but I am finding serial correlations of the residuals have increased(now almost all of them are significant at 5% level).So it appears GARCH modeling is taking care of GARCH effect but spuriously introducing serial correlation in the residuals.
I have checked with model specifications ..theoretically it seems ok and this phenomena is true for 3 stocks out of 9. Rest 6 are yielding  nice/good results in terms no serial correlation in both residuals and squared residuals.
So where the estimation/ GARCH modeling is going wrong? Why this is happening.Anyidea?
Also if the sum of the coefficients (constant+ ARCH term + GARCH Term) is greater than one(1) then what does this imply? Should I Go for an I-GARCH model even if my dependent variable in the mean equation is I(0).
 Kaushik Bhattacharjee



      
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