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

Marco Bee, Julien Hambuckers and Luca Trapin

No 2018/08, DEM Working Papers from Department of Economics and Management

Abstract: TThe g-and-h distribution is a flexible model with desirable theoretical properties. Especially, it is able to handle well the complex behavior of loss data and it is suitable for VaR estimation when large skewness and kurtosis are at stake. However, parameter estimation is di cult, because the density cannot be written in closed form. In this paper we develop an indirect inference method using the skewed- t distribution as instrumental model. We show that the skewed-t is a well suited auxiliary model and study the numerical issues related to its implementation. A Monte Carlo analysis and an application to operational losses suggest that the indirect inference estimators of the parameters and of the VaR outperform the quantile-based estimators.

Keywords: Value-at-Risk; g-and-h distribution; loss model; indirect infer- ence; simulation; intractable likelihood (search for similar items in EconPapers)
JEL-codes: C15 C46 C51 G22 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:trn:utwprg:2018/08

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