Bayesian CV@R/super-quantile regression

Mike Tsionas and Marwan Izzeldin

Journal of Applied Statistics, 2018, vol. 45, issue 16, 2943-2957

Abstract: 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.

Date: 2018
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DOI: 10.1080/02664763.2018.1450363

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