Article (Scientific journals)
Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach
Bee, Marco; Hambuckers, Julien; Trapin
2019In Quantitative Finance
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Keywords :
Value-at-Risk; g-and-h distribution; indirect inference; loss model
Abstract :
[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.
Disciplines :
Quantitative methods in economics & management
Author, co-author :
Bee, Marco;  University of Trento > Department of Economics and Management
Hambuckers, Julien ;  Université de Liège - ULiège > HEC Liège : UER > Finance de Marché
Trapin;  Università Cattolica del Sacro Cuore > Department of Economic Policy
Language :
English
Title :
Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach
Publication date :
2019
Journal title :
Quantitative Finance
ISSN :
1469-7688
eISSN :
1469-7696
Publisher :
Taylor & Francis, United Kingdom
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
DFG - Deutsche Forschungsgemeinschaft [DE]
Available on ORBi :
since 06 February 2019

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