Tail conditional moments for elliptical and log-elliptical distributions

Research output: Contribution to journalArticlepeer-review


In this paper we provide the tail conditional moments for the class of elliptical distributions, which was introduced in Kelker (1970) and was widely discussed in Gupta et al. (2013) and for the class of log-elliptical distributions. These families of distributions include some important members such as the normal, Student-t, logistic, Laplace, and log-normal distributions. We give analytic formulae for the nth higher order unconditional moments of elliptical distributions, which has not been provided before. We also propose novel risk measures, the tail conditional skewness and the tail conditional kurtosis, for examining the skewness and the kurtosis of the tail of loss distributions, respectively.

Original languageEnglish
Pages (from-to)179-188
Number of pages10
JournalInsurance: Mathematics and Economics
StatePublished - 1 Nov 2016

Bibliographical note

Publisher Copyright:
© 2016 Elsevier B.V.


  • Elliptical distributions
  • Log-elliptical distributions
  • Tail conditional expectation
  • Tail conditional moments
  • Tail variance

ASJC Scopus subject areas

  • Statistics and Probability
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Tail conditional moments for elliptical and log-elliptical distributions'. Together they form a unique fingerprint.

Cite this