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.
Bee, M., Hambuckers, J., Trapin, L., Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach, <<QUANTITATIVE FINANCE>>, 2019; 19 (8): 1255-1266. [doi:10.1080/14697688.2019.1580762] [http://hdl.handle.net/10807/141449]
Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach
Trapin, Luca
2019
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.
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