Moments expansion densities for quantifying financial risk

Ñíguez, T.M. and Perote, J. (2017) Moments expansion densities for quantifying financial risk. North American Journal of Economics and Finance, 42. pp. 53-69. ISSN 1062-9408

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Official URL: https://dx.doi.org/10.1016/j.najef.2017.06.002

Abstract

We propose a novel semi-nonparametric distribution that is feasibly parameterized to represent the non-Gaussianities of the asset return distributions. Our Moments Expansion (ME) density presents gains in simplicity attributable to its innovative polynomials, which are defined by the difference between the nth power of the random variable and the nth moment of the density used as the basis. We show that the Gram-Charlier distribution is a particular case of the ME-type of densities. The latter being more tractable and easier to implement when quadratic transformations are used to ensure positiveness. In an empirical application to asset returns, the ME model outperforms both standard and non-Gaussian GARCH models along several risk forecasting dimensions.

Item Type: Article
Uncontrolled Keywords: GARCH; Gram-Charlier series; High-order moments; non-Gaussian distributions; Semi-nonparametric methods; Value-at-Risk.;
Subjects: University of Westminster > Westminster Business School
SWORD Depositor: repository@westminster.ac.uk
Depositing User: repository@westminster.ac.uk
Date Deposited: 19 Jul 2017 10:26
Last Modified: 03 Aug 2017 14:35
URI: http://westminsterresearch.wmin.ac.uk/id/eprint/19324

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