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. (2019). Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach. QUANTITATIVE FINANCE, 19(8), 1255-1266 [10.1080/14697688.2019.1580762].

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.
2019
Bee, M., Hambuckers, J., Trapin, L. (2019). Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach. QUANTITATIVE FINANCE, 19(8), 1255-1266 [10.1080/14697688.2019.1580762].
Bee, Marco; Hambuckers, Julien; Trapin, Luca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/714985
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