In this study, we propose a new semi-nonparametric (SNP) density model for describing the density of portfolio returns. This distribution, which we refer to as the multivariate moments expansion (MME), admits any non-Gaussian (multivariate) distribution as its basis because it is specified directly in terms of the basis density’s moments. To obtain the expansion of the Gaussian density, the MME is a reformulation of the multivariate Gram–Charlier (MGC), but the MME is much simpler and tractable than the MGC when positive transformations are used to produce well-defined densities. As an empirical application, we extend the dynamic conditional equicorrelation (DECO) model to an SNP framework using the MME. The resulting model is parameterized in a feasible manner to admit two-stage consistent estimation and it represents the DECO as well as the salient non-Gaussian features of portfolio return distributions. The in- and out-of-sample performance of a MME–DECO model of a portfolio of 10 assets demonstrate that it can be a useful tool for risk management purposes.

This study presents a methodology for analyzing procyclical systemic risk arising from joint monetary and prudential policy decisions. We analyze the impact of different scenarios of the monetary policy interest rate on the leverage ratio of US commercial banks. The Dynamic Conditional Correlation - Semi-nonparametric model and bivariate spectral analysis are applied to model the dynamics among the variables. The results indicate that high and low interest rates increase leverage while medium rates reduce it. The importance of considering asymmetries and heavy tails of probability distributions in stress tests and the dynamics of the correlation between variables is highlighted when assessing financial stability.