A Normal Weighted Inverse Gaussian Distribution for Skewed and Heavy-Tailed Data

Maina, Calvin B. and Weke, Patrick G. O. and Ogutu, Carolyne A. and Ottieno, Joseph A. M. (2022) A Normal Weighted Inverse Gaussian Distribution for Skewed and Heavy-Tailed Data. Applied Mathematics, 13 (02). pp. 163-177. ISSN 2152-7385

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Abstract

High frequency financial data is characterized by non-normality: asymmetric, leptokurtic and fat-tailed behaviour. The normal distribution is therefore inadequate in capturing these characteristics. To this end, various flexible distributions have been proposed. It is well known that mixture distributions produce flexible models with good statistical and probabilistic properties. In this work, a finite mixture of two special cases of Generalized Inverse Gaussian distribution has been constructed. Using this finite mixture as a mixing distribution to the Normal Variance Mean Mixture we get a Normal Weighted Inverse Gaussian (NWIG) distribution. The second objective, therefore, is to construct and obtain properties of the NWIG distribution. The maximum likelihood parameter estimates of the proposed model are estimated via EM algorithm and three data sets are used for application. The result shows that the proposed model is flexible and fits the data well.

Item Type: Article
Subjects: Oalibrary Press > Mathematical Science
Depositing User: Managing Editor
Date Deposited: 26 Dec 2022 05:25
Last Modified: 19 Jul 2024 06:50
URI: http://asian.go4publish.com/id/eprint/582

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