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.File in questo prodotto:
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