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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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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Applied Statistics
Additional Information:
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 20/03/2018, available online: http://www.tandfonline.com/10.1080/02664763.2018.1450363
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
124204
Deposited By:
Deposited On:
26 Mar 2018 07:54
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Sep 2020 04:10