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Multivariate moments expansion density: Application of the dynamic equicorrelation model

Trino-Manuel Ñíguez and Javier Perote

Journal of Banking & Finance, 2016, vol. 72, issue S, S216-S232

Abstract: 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.

Keywords: Density forecasting; Dynamic equicorrelation; Gram–Charlier series; Multivariate GARCH; Semi-nonparametric methods (search for similar items in EconPapers)
JEL-codes: C16 G1 (search for similar items in EconPapers)
Date: 2016
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