Multivariate moments expansion density: application of the dynamic equicorrelation model

Trino Ñíguez Grau and Javier Perote

No 1602, Working Papers from Banco de España

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 demonstrates that it can be a useful tool for risk management purposes.

Keywords: density forecasting; dynamic equicorrelation; Gram-Charlier series; multivariate GARCH; semi-nonparametric method (search for similar items in EconPapers)
JEL-codes: C16 G1 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2016-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (20)

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Journal Article: Multivariate moments expansion density: Application of the dynamic equicorrelation model (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:bde:wpaper:1602

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