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• W2891292858 abstract "Abstract Risk measures such as value-at-risk (VaR) and expected shortfall (ES) may require the calculation of quantile functions from quantile regression models. In a parametric set-up, we propose to regress directly on the quantiles of a distribution and demonstrate a method through the conditional autoregressive range model which has increasing popularity in recent years. Two flexible distribution families: the generalised beta type two on positive support and the generalised-t on real support (which requires log transformation) are adopted for the range data. Then the models are extended to allow the volatility dynamic and compared in terms of goodness-of-fit. The models are implemented using the module fmincon in Matlab under the classical likelihood approach and applied to analyse the intra-day high-low price ranges from the All Ordinaries index for the Australian stock market. Quantiles and upper-tail conditional expectations evaluated via VaR and ES respectively are forecast using the proposed models." @default.
• W2891292858 created "2018-09-27" @default.
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• W2891292858 date "2018-09-06" @default.
• W2891292858 modified "2023-09-27" @default.
• W2891292858 title "Efficient estimation of financial risk by regressing the quantiles of parametric distributions: An application to CARR models" @default.
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• W2891292858 doi "https://doi.org/10.1515/snde-2017-0012" @default.
• W2891292858 hasPublicationYear "2018" @default.
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