Work

A Subcopula Characterization of Dependence for the Multivariate Bernoulli Distribution

Year: 2026

Type: article

Abstract: This paper provides a subcopula characterization of dependence for the Multivariate Bernoulli Distribution. Explicit formulas are derived using subcopulas to introduce dependence measures for interactions of all orders, not just pairwise. A Bayesian inference approach is also applied to estimate the parameters, offering practical tools for parameter estimation and dependence analysis in real-world applications. The main contribution is an explicit, order-by-order characterization of multivariate dependence for binary data (beyond pairwise association), linking joint probabilities to subcopula-based dependence parameters/measures under full compatibility; this fills a gap in the literature where practical modeling often stops at pairwise dependence.

Source: Journal of Statistical Theory and Applications

DOI: 10.1007/s44199-025-00157-4

Author: Arturo Erdely

Institutions: Facultad de Estudios Superiores Acatlán, Universidad Nacional Autónoma de México

Language: English

Topic: Financial Risk and Volatility Modeling

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,490

Funders: Universidad Nacional Autónoma de México, Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México