Multivariate Moments Expansion Density: Application of the Dynamic Equicorrelation Model

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.

Urban transformations and rail stations system - the study case of Naples

The aim of this study is to give an interpretation of the urban transformations connected to rail transit system investments; in particular the main research goal is to analyze and give a methodological support for the urban transformation phenomena government in the rail transit stations areas. The article proposes an empirical studies comparative analysis and an application in the Naples urban area, in which a new rail transit network has been developed. In particular the socio-economic transit impacts on the urban system are measured and interpretated with the support of a GIS; therefore an application of the node-place interpretative model (Bertolini 1999) is proposed in order to support transit–land use planning processes in the stations areas.