Reference : Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach
Scientific journals : Article
Business & economic sciences : Quantitative methods in economics & management
http://hdl.handle.net/2268/232630
Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach
English
Bee, Marco [University of Trento > Department of Economics and Management > > >]
Hambuckers, Julien mailto [Université de Liège - ULiège > HEC Liège : UER > Finance de Marché >]
Trapin [Università Cattolica del Sacro Cuore > Department of Economic Policy > > >]
2019
Quantitative Finance
Taylor & Francis
Yes (verified by ORBi)
International
1469-7688
1469-7696
United Kingdom
[en] Value-at-Risk ; g-and-h distribution ; indirect inference ; loss model
[en] The 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. However, parameter estimation is difficult, 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.
DFG - Deutsche Forschungsgemeinschaft
http://hdl.handle.net/2268/232630
10.1080/14697688.2019.1580762

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