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

^*Corresponding author

Faculty of Economics

University of Trento
University of Göttingen
University of Liege

Research output: Contribution to journal › Article

2 Citations (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.

Original language	English
Pages (from-to)	1255-1266
Number of pages	12
Journal	Quantitative Finance
Volume	19
Issue number	8
DOIs	https://doi.org/10.1080/14697688.2019.1580762
Publication status	Published - 2019

All Science Journal Classification (ASJC) codes

Finance
General Economics,Econometrics and Finance

Keywords

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

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10.1080/14697688.2019.1580762

Cite this

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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.",

keywords = "Value-at-Risk, g-and-h distribution, indirect inference, loss model, Value-at-Risk, g-and-h distribution, indirect inference, loss model",

author = "M. Bee and J. Hambuckers and Luca Trapin",

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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

All Science Journal Classification (ASJC) codes

Keywords

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