Modeling right-skewed financial data stream : a likelihood inference based on the generalized Birnbaum–Saunders mixture model
Loading...
Date
Authors
Naderi, Mehrdad
Hashemi, Farzane
Bekker, Andriette, 1958-
Jamalizadeh, Ahad
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Finite mixture models have recently been considered for analyzing positive support economical data streams with non-normal features. In this paper, a new mixture model based on the novel class of generalized Birnbaum–Saunders distributions is proposed to enhance strength and flexibility in modeling heterogeneous lifetime data. Some characteristics and properties of this mixture model are outlined. By presenting a convenient hierarchical representation, a mathematically elegant and computationally tractable EM-type algorithm is adopted for computing maximum likelihood estimates. Theoretical formulae of well-known risk measures referring to the class of generalized Birnbaum–Saunders distributions are derived. Finally, the utility of the postulated methodology is illustrated with some real-world data examples.
Description
Keywords
Birnbaum–Saunders distribution, Finite mixture model, Normal mean-variance model, Risk measurement, Tail-value-at-Risk (TVaR), Value-at-risk (VaR)
Sustainable Development Goals
Citation
Naderi, M., Hashemi, F., Bekker, A. et al. 2020, 'Modeling right-skewed financial data stream : a likelihood inference based on the generalized Birnbaum–Saunders mixture model', Applied Mathematics and Computation, vol. 376, art. 125109, pp. 1-16.