2019/12/15 by Chandima N. P. G. Arachchige, Arachchige, Chandima N. P. G., Luke A. Prendergast +1
Economics, Econometrics and Finance · Mathematics · #Advanced Statistical Methods and Models #FOS: Mathematics #Financial Risk and Volatility Modeling #Statistical Distribution Estimation and Applications #Statistics Theory (math.ST) #math.ST #stat.TH
paper · pdf · doi:10.48550/arxiv.1912.06996
openalex publication_date 2019/12/15 · arxiv created 2019/12/17 · arxiv updated 2019/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Skewness measures can be used to measure the level of asymmetry of a distribution. Given the prevalence of statistical methods that assume underlying symmetry, and also the desire for symmetry in order to make meaningful judgements for common summary measures (e.g. the sample mean), reliably quantifying asymmetry is an important problem. There are several measures, among them generalizations of Bowley's well known skewness coefficient, that use sample quartiles and other quantile-based measures. The main drawbacks of many measures is that they are either limited to quartiles and do not take into account more extreme tail behavior, or that they require one to choose other quantiles (i.e. choose a value for p different from 0.25) in place of the quartiles. Our objective is to (i) average the skewness measures over all p and (ii) provide interval estimators for the new measure with good coverage properties. Our simulation results show that the interval estimators perform very well for all distributions considered.