Artikel

Modeling multivariate financial series and computing risk measures via Gram-Charlier-like expansions

This paper develops an approach based on Gram-Charlier-like expansions for modeling financial series to take in due account features such as leptokurtosis. A Gram-Charlier-like expansion adjusts the moments of interest of a given distribution via its own orthogonal polynomials. This approach, formerly adopted for univariate series, is here extended to a multivariate context by means of spherical densities. Previous works proposed the Gram-Charlier of the multivariate Gaussian, obtained by using Hermite polynomials. This work shows how polynomial expansions of an entire class of spherical laws can be worked out with the aim of obtaining a wide set of leptokurtic multivariate distributions. A Gram-Charlier-like expansion is a distribution characterized by an additional parameter with respect to the parent spherical law. This parameter, which measures the increase in kurtosis due to the polynomial expansion, can be estimated so as to make the resulting distribution capable of describing the empirical kurtosis found in the data. An application of the Gram-Charlier-like expansions to a set of financial assets proves their effectiveness in modeling multivariate financial series and assessing risk measures, such as the value at risk and the expected shortfall.

Language
Englisch

Bibliographic citation
Journal: Risks ; ISSN: 2227-9091 ; Volume: 8 ; Year: 2020 ; Issue: 4 ; Pages: 1-21 ; Basel: MDPI

Classification
Wirtschaft
Econometrics
Model Construction and Estimation
Financial Econometrics
Subject
orthogonal polynomials
kurtosis
power raised hyperbolic-secant distributions
value at risk
expected shortfall

Event
Geistige Schöpfung
(who)
Zoia, Maria Grazia
Vacca, Gianmarco
Barbieri, Laura
Event
Veröffentlichung
(who)
MDPI
(where)
Basel
(when)
2020

DOI
doi:10.3390/risks8040123
Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Zoia, Maria Grazia
  • Vacca, Gianmarco
  • Barbieri, Laura
  • MDPI

Time of origin

  • 2020

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