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Modeling Portfolios with Leptokurtic and Dependent Risk Factors

Piero Quatto, Gianmarco Vacca, Maria Grazia Zoia

arXiv 8 Jun 2021 · Econometrics

arXiv:2106.04218 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Recently, an approach to modeling portfolio distribution with risk factors distributed as Gram-Charlier (GC) expansions of the Gaussian law, has been conceived. GC expansions prove effective when dealing with moderately leptokurtic data. In order to cover the case of possibly severe leptokurtosis, the so-called GC-like expansions have been devised by reshaping parent leptokurtic distributions by means of orthogonal polynomials specific to them. In this paper, we focus on the hyperbolic-secant (HS) law as parent distribution whose GC-like expansions fit with kurtosis levels up to 19.4. A portfolio distribution has been obtained with risk factors modeled as GClike expansions of the HS law which duly account for excess kurtosis. Empirical evidence of the workings of the approach dealt with in the paper is included.

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24
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33
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
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4Joe, H., Xu, J.J (1996) The Estimation Method of Inference Functions for Margins for Multivariate Models0.51121100%
5Acerbi, C., Tasche, D (2002) On the coherence of expected shortfall0.40511100%
6Acerbi, C., Székely, B (2014) Back-testing expected shortfall0.40511100%
7Balanda, K.P., MacGillivray, H (1988) Kurtosis: a critical review0.40511100%
8Ding, P (2014) Three occurrences of the Hyperbolic-Secant distribution0.40511100%
9Dodd, E.L (1925) The Frequency Law of a Function of Variables With Given Frequency Laws0.40511100%
10Finucan, H (1964) A note on kurtosis0.40511100%

Showing the top 10 of 24 scored citations.