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ISyE 6402 UNIT 4_ Modeling Heteroskedasticit WITH 100- SURE ANSWERS
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ISyE 6402 UNIT 4_ Modeling Heteroskedasticit WITH 100- SURE ANSWERS
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10/6/24, 7:49 AMEUNICE
ISyE 6402 UNIT 4: Modeling Heteroskedasticity WITH
QUESTIONS AND 100% SURE ANSWERS
Terms in this set (29)
The ACF plot of the residuals looks like one for the
white noise process. On the other hand, the ACF
plot of the squared residuals suggest that there is
how can you identify independence via
a serial dependence in the squared residuals. Thus,
ACF plot?
the residuals are linearly uncorrelated, but not
independent since the transformed residuals,
specifically the squared residuals are correlated.
Null of non-correlated residuals
Does NOT test for independence
We apply here the Box-Ljung test. Note that the test is not for testing independence,
Box-Llung test use but for testing whether the data are uncorrelated where the null hypothesis is that
the data aren't correlated versus the alternative that the data are correlated. Based
on this test, the p-value for testing uncorrelated scored residuals is very small
indicating that we reject the new hypothesis. And thus, conclude that the squared
residuals are correlated.
using non-parametric regression
how do we estimate variance?
see image - big peaks on the parametric curve
estimated parametric volatility vs. align w/ volatility in the price
log(price)
what does the ARCH model do? model conditional variance
see image
Modeling heteroskedasticity
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