 Multivariate moments expansion density: Application of the dynamic equicorrelation modelTrino Ñíguez Grau 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) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:72:y:2016:i:s:p:s216-s232 DOI: 10.1016/j.jbankfin.2015.12.012 Access Statistics for this article Journal of Banking & Finance is currently edited by Ike Mathur More articles in Journal of Banking & Finance from Elsevier |
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