Document Type

Article

Publication Date

2026

DOI

10.3390/jrfm19010027

Publication Title

Journal of Risk and Financial Management

Volume

19

Issue

1

Pages

27

Abstract

Time series analysis is crucial for modeling and forecasting diverse real-world phenomena. Traditional models typically assume continuous-valued data; however, many applications involve integer-valued series, often including negative integers. This paper introduces an approach that combines copula theory with the bivariate Skellam distribution to handle such integer-valued data effectively. Copulas are widely recognized for capturing complex dependencies among variables. By integrating copulas, our proposed method respects integer constraints while modeling positive, negative, and temporal dependencies accurately. Through simulation and an empirical study on a real-life example, we demonstrate that our class of models performs well. This approach has broad applicability in areas such as finance, epidemiology, and environmental science, where modeling series with integer values, both positive and negative, is essential.

Rights

© 2026 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "Data and R code used in this study are available from the corresponding author upon reasonable request."

Original Publication Citation

Alqawba, M., Diawara, N., & Mor Sene, M. (2026). Integer-valued time series model via copula-based bivariate Skellam distribution. Journal of Risk and Financial Management, 19(1), Article 27. https://doi.org/10.3390/jrfm19010027

ORCID

0000-0002-8403-6793 (Diawara)

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