The multivariate tail-inflated normal distribution and its application in finance

TL;DR

This paper introduces the multivariate tail-inflated normal (MTIN) distribution, a heavy-tailed elliptical model with a simple form and flexible tail behavior, useful for robust financial data modeling.

Contribution

It presents the MTIN distribution with a closed-form density, discusses estimation methods including EM, and demonstrates its application and advantages in financial data analysis.

Findings

MTIN distribution has flexible tail-weight controlled by one parameter.

Maximum likelihood and method of moments estimators are developed and compared.

MTIN effectively models financial data, outperforming some existing elliptical distributions.

Abstract

This paper introduces the multivariate tail-inflated normal (MTIN) distribution, an elliptical heavy-tails generalization of the multivariate normal (MN). The MTIN belongs to the family of MN scale mixtures by choosing a convenient continuous uniform as mixing distribution. Moreover, it has a closed-form for the probability density function characterized by only one additional ``inflation'' parameter, with respect to the nested MN, governing the tail-weight. The first four moments are also computed; interestingly, they always exist and the excess kurtosis can assume any positive value. The method of moments and maximum likelihood (ML) are considered for estimation. As concerns the latter, a direct approach, as well as a variant of the EM algorithm, are illustrated. The existence of the ML estimates is also evaluated. Since the inflation parameter is estimated from the data, robust estimates of the mean vector and covariance matrix of the nested MN distribution are automatically obtained by down-weighting. Simulations are performed to compare the estimation methods/algorithms and to investigate the ability of AIC and BIC to select among a set of candidate elliptical models. For illustrative purposes, the MTIN distribution is finally fitted to multivariate financial data where its usefulness is also shown in comparison with other well-established multivariate elliptical distributions.