This article presents a new semi-nonparametric (SNP) density function, named Positive Edgeworth-Sargan (PES). We show that this distribution belongs to the family of (positive) Gram-Charlier (GC) densities and thus it preserves all the good properties of this type of SNP distributions but with a much simpler structure. The in-and out-of-sample performance of the PES is compared with symmetric and skewed GC distributions and other widely used densities in economics and finance. The results confirm the PES as a good alternative to approximate financial returns distribution, specially when skewness is not severe.Å We thank a co-editor, two anonymous referees,

This paper examines the e¤ects of higher-order risk attitudes and statistical moments on the optimal allocation of risky assets within the standard portfolio choice model. We derive the expressions for the optimal proportion of wealth invested in the risky asset to show they are functions of portfolio returns third-and fourth-order moments as well as on the investor's risk preferences of prudence and temperance. We illustrate the relative importance that the introduction of those higher-order e¤ects have in the decision of expected utility maximizers using data for the US.JEL classi…cation: C14, G11.

This paper generalizes the Dynamic Conditional Correlation (DCC) model of Engle (2002) to incorporate a ‡exible non-Gaussian distribution based on Gram-Charlier expansions. The resulting semi-nonparametric (SNP)-DCC model formally admits a separate estimation of, in a …rst stage, the individual conditional variances under a Gaussian distribution and, in the second stage, the conditional correlations and the rest of the density parameters, thus overcoming the known "dimensionality curse" of the multivariate GARCH models. Furthermore the proposed SNP-DCC solves the negativity problem inherent to truncated SNP densities providing a general parametric structure that may accurately approximate a target heavy-tailed distribution. We test the performance of a (positive) SNP-DCC model with respect to the (Gaussian)-DCC model through an empirical application of density forecasting for portfolio asset returns data. Our results show that the proposed multivariate model is more ‡exible and provides a better …t and forecast of the portfolio returns distribution tails, being thus useful for …nancial risk forecasting and evaluation.JEL classi…cation: C16, G1.

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 speci…ed 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-de…ned 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.

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