A Reexamination of Finite- and Infinite-Variance Distributions as Models of Daily Stock Returns.

This study investigates the general (asymmetric) stable Paretian distribution and three finite-variance, time-independent distributions applied to daily stock-return series. Previous empirical comparisons have, in general, ignored the existence and effects of skewness on the process parameters of stable laws. The results of log-likelihood ratio and log-odds tests indicate that finite-variance models still dominate after accounting for documented skewness. In particular, the mixed diffusion-jump and compound normal models appear to be the most descriptive time-independent models.