Every week, we select a recently published Open Access article to feature. This week’s article comes from Statistica Neerlandica and reviews skewness in financial markets through framework assessment and a new measure based on variance decomposition 

The article’s abstract is given below, with the full article available to read here. 

Campisi, G., La Rocca, L., & Muzzioli, S. (2022). Assessing skewness in financial markets. Statistica Neerlandica, 1– 23. https://doi.org/10.1111/stan.12273   

It is a matter of common observation that investors value substantial gains but are averse to heavy losses. Obvious as it may sound, this translates into an interesting preference for right-skewed return distributions, whose right tails are heavier than their left tails. Skewness is thus not only a way to describe the shape of a distribution, but also a tool for risk measurement. We review the statistical literature on skewness and provide a comprehensive framework for its assessment. Then, we present a new measure of skewness, based on the decomposition of variance in its upward and downward components. We argue that this measure fills a gap in the literature and show in a simulation study that it strikes a good balance between robustness and sensitivity.