On minimum correlation in construction of multivariate distributions

In this paper we present a method for exact generation of multivariate samples with pre-specified marginal distributions and a given correlation matrix, based on a mixture of Fr'echet-Hoeffding bounds and marginal products. The bivariate algorithm can accommodate any among the theoretically possible correlation coefficients, and explicitly provides a connection between simulation and the minimum correlation attainable for different distribution families. We calculate the minimum correlations in several common distributional examples, including in some that have not been looked at before. As an illustration, we provide the details and results of implementing the algorithm for generating three-dimensional negatively and positively correlated Beta random variables, making it the only non-copula algorithm for correlated Beta simulation in dimensions greater than two. This work has potential for impact in a variety of fields where simulation of multivariate stochastic components is desired.

💡 Research Summary

The paper introduces a novel exact simulation method for generating multivariate random vectors with arbitrary prescribed marginal distributions and a target correlation matrix. The core idea is to construct the joint distribution as a convex combination of the Fréchet‑Hoeffding upper bound (perfect positive dependence), the Fréchet‑Hoeffding lower bound (perfect negative dependence), and the product of the marginals (independence). For any pair of marginal distributions, the feasible correlation range (