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

[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

Available on ORBi :

since 06 February 2019

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