Using Tukey's g and h family of distributions to calculate value-at-risk and conditional value-at-risk

Moscoso, José Alfredo Jiménez; Arunachalam, Viswanathan

doi:10.21314/jor.2011.230

Cited by 29 publications

(2 citation statements)

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“…Within this family of distributions, two particular subfamilies have received the most attention in the literature; these correspond to the g-and-h and the g-and-k distributions. The first of these families the g-and-h has been studied in several contexts (see, for instance, the developments in the areas of risk and insurance modelling in (Dutta and Perry 2006;Peters and Sisson 2006;Degen et al 2007;Jiménez and Arunachalam 2011) and the detailed discussion in (Cruz et al 2015, chp. 9)).…”

Section: Generalized Elongation Deformation Quantile Error Familiesmentioning

confidence: 99%

General Quantile Time Series Regressions for Applications in Population Demographics

Peters

2018

Risks

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The paper addresses three objectives: the first is a presentation and overview of some important developments in quantile times series approaches relevant to demographic applications—secondly, development of a general framework to represent quantile regression models in a unifying manner, which can further enhance practical extensions and assist in formation of connections between existing models for practitioners. In this regard, the core theme of the paper is to provide perspectives to a general audience of core components that go into construction of a quantile time series model. The third objective is to compare and discuss the application of the different quantile time series models on several sets of interesting demographic and mortality related time series data sets. This has relevance to life insurance analysis and the resulting exploration undertaken includes applications in mortality, fertility, births and morbidity data for several countries, with a more detailed analysis of regional data in England, Wales and Scotland.

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Section: Generalized Elongation Deformation Quantile Error Familiesmentioning

confidence: 99%

General Quantile Time Series Regressions for Applications in Population Demographics

Peters

2018

Risks

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“…A range of distributions that are commonly used for operational risk capital calculation (lognormal, Weibull, log gamma, generalized Pareto (GP), lognormal mixture, Burr, loglogistic, Gumbel, Fréchet and g-and-h) were selected. Tukey's g-and-h distribution (Jiménez and Arunachalam 2011) is peculiar in that it is very difficult to find an initial parameterization to achieve a data fit; further, it can be useful for fits to distributions with both large outlier losses and small losses. For each distribution, random losses were generated.…”

Section: Configuration For the Body/tail Weightmentioning

confidence: 99%

A central limit theorem formulation for empirical bootstrap value-at-risk

Mitic¹,

Bloxham²

2018

JRMV

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In this paper, the importance of the empirical bootstrap (EB) in assessing minimal operational risk capital is discussed, and an alternative way of estimating minimal operational risk capital using a central limit theorem (CLT) formulation is presented. The results compare favorably with risk capital obtained by fitting appropriate distributions to the same data. The CLT formulation is significant in validation because it provides an alternative approach to the calculation that is independent of both the empirical severity distribution and any dependent fitted distribution.

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2015

Fundamental Aspects of Operational Risk and Insurance Analytics

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Using Tukey's g and h family of distributions to calculate value-at-risk and conditional value-at-risk

Cited by 29 publications

References 0 publications

General Quantile Time Series Regressions for Applications in Population Demographics

General Quantile Time Series Regressions for Applications in Population Demographics

A central limit theorem formulation for empirical bootstrap value-at-risk

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