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Moments expansion densities for quantifying financial risk

Trino-Manuel Ñíguez and Javier Perote ()

The North American Journal of Economics and Finance, 2017, vol. 42, issue C, 53-69

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.

Keywords: GARCH; Gram–Charlier series; High-order moments; non-Gaussian distributions; Semi-nonparametric methods; Value-at-Risk (search for similar items in EconPapers)
JEL-codes: C16 C53 G12 (search for similar items in EconPapers)
Date: 2017
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Handle: RePEc:eee:ecofin:v:42:y:2017:i:c:p:53-69