Hi,

I have times series of a stock return, and I want to detect if there is any effect on the month January on this stock return (January effect). I have created dummies for the days in January.
If I want to perform regular OLS, I have to detect if there are any heteroskedasticity and autocorrelation:

First I fin out if there is any heteroskedasticity:

Code:
reg RetOSEBX JanOSEBX
estat hettest 

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity 
         Ho: Constant variance
         Variables: fitted values of RetOSEBX

         chi2(1)      =     0.21
         Prob > chi2  =   0.6463
Okey, fine, no heteroskedasticity. Then I test for autocorrelation:
Code:
estat bgodfrey

Breusch-Godfrey LM test for autocorrelation
---------------------------------------------------------------------------
    lags(p)  |          chi2               df                 Prob > chi2
-------------+-------------------------------------------------------------
       1     |          0.651               1                   0.4198
---------------------------------------------------------------------------
                        H0: no serial correlation
As we observe, no autocorrelation either. But when I do the test for ARCH effects I get the following:

Code:
estat archlm
LM test for autoregressive conditional heteroskedasticity (ARCH)
---------------------------------------------------------------------------
    lags(p)  |          chi2               df                 Prob > chi2
-------------+-------------------------------------------------------------
       1     |        462.568               1                   0.0000
---------------------------------------------------------------------------
         H0: no ARCH effects      vs.  H1: ARCH(p) disturbance
Here we get that there is ARCH effect. How can this be the case when there is no autocorrelation? Doesnt ARCH implicit tell that there is also autocorrelation in the model?