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.