Effects of heteroskedasticity and autocorrelation on Regression

CFA Level II Quant
#1

Hello,

Can anyone explain why are the standard errors too small when we have either Heteroskedasticity or autocorrelation ?

Both the CFA curriculum and Schweser notes state that they are too small (leading to large test statistic and thus to Type I error) but they don’t say why they are too small.

Thanks.

Regards,

#2

In autocorrelation, data tends to hang close to each other, since they are dependent on the prior data point, thus the small errors. That’s how i remember it.

As for heterosked… I think the residuals can be either too small or too big.