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Article

Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series

1
Department of Economics and Statistics, University of Naples “Federico II”, 80126 Naples, Italy
2
Intelligent Data Science and Artificial Intelligence Research Center, Universitat Politecnica de Catalunya, 08034 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Nicola Maria Rinaldo Loperfido and José Carlos R. Alcantud
Symmetry 2021, 13(6), 959; https://doi.org/10.3390/sym13060959
Received: 30 March 2021 / Revised: 18 May 2021 / Accepted: 24 May 2021 / Published: 28 May 2021
(This article belongs to the Special Issue Symmetry and Asymmetry in Multivariate Statistics and Data Science)
The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties, such as heavy tails and skewness. Thanks to its flexibility, the skewed exponential power distribution (also called skewed generalized error distribution) ensures a unified and general framework for clustering possibly skewed and heavy tailed time series. This paper develops a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is shown by means of application to financial time series, demonstrating also how the obtained clusters can be used to form portfolio of stocks. View Full-Text
Keywords: classification; generalized error distribution; skewness; skewed exponential power distribution; financial time series; portfolio selection classification; generalized error distribution; skewness; skewed exponential power distribution; financial time series; portfolio selection
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MDPI and ACS Style

Mattera, R.; Giacalone, M.; Gibert, K. Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series. Symmetry 2021, 13, 959. https://doi.org/10.3390/sym13060959

AMA Style

Mattera R, Giacalone M, Gibert K. Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series. Symmetry. 2021; 13(6):959. https://doi.org/10.3390/sym13060959

Chicago/Turabian Style

Mattera, Raffaele, Massimiliano Giacalone, and Karina Gibert. 2021. "Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series" Symmetry 13, no. 6: 959. https://doi.org/10.3390/sym13060959

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