Bayesian CV@R/super-quantile regression

Tsionas, Efthymios and Izzeldin, Marwan (2018) Bayesian CV@R/super-quantile regression. Journal of Applied Statistics. ISSN 0266-4763

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In this paper we provide a Bayesian interpretation of the conditional value at risk, CV@R, or super-quantile regression recently developed by Rockafellar et al. [Super-quantile regression with applications to buffered reliability, uncertainty quantification, and conditional value-at-risk, Eur. J. Oper. Res. 234 (2014), pp. 140–154]. Computations are based on particle filtering using a special posterior distribution consistent with the super-quantile concept. An empirical application to data used by RRM as well to another data set on energy prices confirms their results and shows the applicability of the new techniques.

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Journal Article
Journal or Publication Title:
Journal of Applied Statistics
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This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 20/03/2018, available online:
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Deposited On:
26 Mar 2018 07:54
Last Modified:
11 May 2022 05:49