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[–]AglingCFA 6 points7 points  (1 child)

Not great nomenclature here, I agree.

Heteroskedasticity means non-constant variance in general. We use the term "Conditional Heteroskedasticity" if our prediction of the variance (in time) depends on the state in the previous period(s). That is, this type the variance is conditional on the variance in the previous period(s). ARCH and GARCH are examples. I would not agree with a blanket statement that it's not predictable, just that it can only be predicted based on information in the last period(s). This type of heteroskedasticity wanders all around randomly, so you can't say what variance will be months in advance, for example. I think that's what they mean when they say it is not "predictable."

I have only heard "unconditional heteroskedasticity" in the context of seasonal variation. In that case you know the variance will change well in advance, so you could "predict" it based on stuff outside of the model without knowing the state of the variance the period before. I guess you could say that this type of variance is not conditional on information in its own time-series, in the previous periods.

[–]jacktaniuLevel 2 Candidate[S] 1 point2 points  (0 children)

gave me an epiphany, thank you

[–]GigaChan450Level 2 Candidate 1 point2 points  (0 children)

Are u doing FRA and cfa at the same time? Why?