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Bayesian CV@R/super-quantile regression

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  • Mike G. Tsionas
  • Marwan Izzeldin

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.

Suggested Citation

  • Mike G. Tsionas & Marwan Izzeldin, 2018. "Bayesian CV@R/super-quantile regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(16), pages 2943-2957, December.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:16:p:2943-2957
    DOI: 10.1080/02664763.2018.1450363
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    Cited by:

    1. Daniel Velásquez-Gaviria & Andrés Mora-Valencia & Javier Perote, 2020. "A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets," Energies, MDPI, vol. 13(11), pages 1-42, June.

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