Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach

M. Bee; J. Hambuckers; Luca Trapin

doi:10.1080/14697688.2019.1580762

Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach

M. Bee, J. Hambuckers, Luca Trapin

Università Cattolica del Sacro Cuore

Risultato della ricerca: Contributo in rivista › Articolo in rivista

2 Citazioni (Scopus)

Abstract

The g-and-h distribution is able to handle well the complex behavior of loss data and applied to operational losses suggests that indirect inference estimators of VaR outperform quantile-based estimators.

Lingua originale	English
pagine (da-a)	1255-1266
Numero di pagine	12
Rivista	Quantitative Finance
Volume	19
DOI	https://doi.org/10.1080/14697688.2019.1580762
Stato di pubblicazione	Pubblicato - 2019

Keywords

Value-at-Risk
g-and-h distribution
indirect inference
loss model

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10.1080/14697688.2019.1580762

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abstract = "The g-and-h distribution is able to handle well the complex behavior of loss data and applied to operational losses suggests that indirect inference estimators of VaR outperform quantile-based estimators.",

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KW - Value-at-Risk

KW - g-and-h distribution

KW - indirect inference

KW - loss model

KW - Value-at-Risk

KW - g-and-h distribution

KW - indirect inference

KW - loss model

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ER -

Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach

Abstract

Keywords

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