Extension of time series models with non-stationary and uncertain variance serves to address their shortcomings when it comes to the handling of non-normality in the data patterns. Many financial series fall into that category and data distributions produce behaviours that fall outside ‘normal’ laws. Mixing standard time series models with heavy tail processes brings the remedy and produces new class of hybrid series that capture the non-normal patterns reasonably well.
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