Heteroscedasticity: Only downside being "Hard to spell"

As mentioned in my last post, volatility of stock returns vary over time (Heteroscedasticity) and they happen in a systematic way so as to ensure mean reversion. The concept is appealing well beyond the regular homoscedastic models which assume constant volatility. And so the best book I had that provided hands on model calibration experience was the elementary “Time Series Analysis by Cryer and Chan”.

Wiki says on the subject:

“The possible existence of heteroscedasticity is a major concern in the application of regression analysis, including the analysis of variance, because the presence of heteroscedasticity can invalidate statistical tests of significance that assume that the modeling errors are uncorrelated and normally distributed and that their variances do not vary with the effects being modeled.”

Tinkering with RSLN-2

I have decided to dedicate a fraction of my time to “Stochastic Modeling:  Theory and Reality from an Actuarial Perspective” book. Even after six years into the profession my shallow perspective tells me that this should be our primary skill set.

As a first step I jumped straight to the section on “Regime Switching Models” to study the RSLN (Regime Switching Log-normal) model. The Actuarial Literature elsewhere tends to be inundated with this model whenever it comes to Stock models. Actuaries seem to be rebels against normal statisticians who would prefer regressive models over probability models.