Analyzing skewed financial data using skew scale-shap mixtures of multivariate normal distributions

This paper introduces an innovative family of statistical models called the multivariate skew scale-shape mixtures of normal distributions. These models serve as a versatile tool in statistical analysis by efficiently characterizing the skewed and leptokurtic nature commonly observed in multivariate datasets. Their applicability shines in real-world scenarios where data often deviate from standard statistical assumptions due to the presence of outliers. We present an EM-type algorithm designed for maximizing likelihood estimation and evaluate the model’s effectiveness through real-world data applications. Through rigorous testing against various datasets, we assess the performance and practicality of the proposed algorithm in real statistical scenarios. The results demonstrate the remarkable performance of this new family of distributions.