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Efficient estimation of financial risk by regressing the quantiles of parametric distributions: An application to CARR models

Jennifer Chan, Nitithumbundit Thanakorn, Peiris Shelton and Kok Haur Ng
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Peiris Shelton: The University of Sydney, School of Mathematics and Statistics, Rm 817 Carslaw Building,Sydney, New South Wales, 2006, Australia

Studies in Nonlinear Dynamics & Econometrics, 2019, vol. 23, issue 2, 22

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.

Keywords: conditional autoregressive range model; generalised beta type two distribution; generalised-t distribution; parametric quantile regression; tail conditional expectation; volatility model (search for similar items in EconPapers)
Date: 2019
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