Mathematics Colloquium

Wednesday, 15 November, 4-5 pm

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Venue: Ramanujan Hall

Hosts: Sanjiv Sabnis and Radhe Srivastava

Speaker: Prof. Sujit Ghosh

Affiliation: North Carolina State University

Title: Multivariate Dependence Beyond Correlation: Nonparametric Copulas

Abstract: In the field of climate, finance, insurance, system reliability, etc., it is often of interest to measure the dependence among variables by modeling a multivariate distribution using a copula. The copula models with parametric assumptions are easy to estimate but can be highly biased when such assumptions are false, while the empirical copulas are non-smooth and often not genuine copula, making the inference about dependence challenging in practice. As a compromise, the empirical Bernstein copula provides a smooth estimator, but the estimation of tuning parameters remains elusive. In this paper, by using the so-called empirical checkerboard copula, we build a hierarchical empirical Bayes model that enables the estimation of a smooth copula function for arbitrary dimensions. The proposed estimator based on the multivariate Bernstein polynomials is itself a genuine copula, and the selection of its dimension-varying degrees is data-dependent. We also show that the proposed copula estimator provides a more accurate estimate of several multivariate dependence measures, which can be obtained in closed form. We investigate the asymptotic and finite-sample performance of the proposed estimator and compare it with some nonparametric estimators through simulation studies. An application to portfolio risk management is presented, along with a quantification of estimation uncertainty.

The presentation is based on a recently published paper with Dr. Lu Lu:
https://doi.org/10.3390/math11204383