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.
Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach / Bee, Marco; Hambuckers, Julien; Trapin, Luca. - In: QUANTITATIVE FINANCE. - ISSN 1469-7688. - STAMPA. - 2019, 19:8(2019), pp. 1255-1266. [10.1080/14697688.2019.1580762]
Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach
Bee, Marco;
2019-01-01
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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