Work

On a Three-Parameter Bounded Gamma–Gompertz Distribution, with Properties, Estimation, and Applications

Year: 2025

Type: article

Abstract: A novel statistical model, the Bounded Gamma–Gompertz Distribution (BGGD), is presented alongside a full characterization of its properties. Our investigation identifies maximum-likelihood estimation (MLE) as the most effective fitting procedure, proving it to be more consistent and efficient than alternative approaches like L-moments and Bayesian estimation. Empirical validation on Tesla (TSLA) financial records—spanning open, high, low, close prices, and trading volume—showcased the BGGD’s superior performance. It delivered a better fit than several competing heavy-tailed distributions, including Student-t, Log-Normal, Lévy, and Pareto, as indicated by minimized AIC and BIC statistics. The results substantiate the distribution’s robustness in capturing extreme-value behavior, positioning it as a potent tool for financial modeling applications.

Source: AppliedMath

DOI: 10.3390/appliedmath5040177

Authors: Tassaddaq Hussain, Mohammad Shakil, Mohammad Ahsanullah

Institutions: Mirpur University of Science and Technology, Miami Dade College, Rider University

Language: English

Topic: Statistical Distribution Estimation and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,207

OpenAlex ID: W7110370604

FWCI:

Cites: 33

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