John Manistre, Mercer Human Resourses
Measures of Covariance in the Tail of a Loss Distribution
This presentation will define measures of associationbetween risks which
generalize the notion of covariance. The measures provide insight into
the way component risks interact when in the tail of the sum of those
risks. The new measures reduce to standard covariance and
correlation measures when the risks have a multivariate normal distribution.
The presentation will show how these ideas can be applied to developing
formula approximations for Conditional Tail Expectation Measures. Some
simple examples will illustrate the ideas.
Philippe Artzner, University of Strasbourg
Multiperiod Risk Measurement: where are we?
After a brief review of the theory of one-period coherent risk measures,
we attend to list the varied questions raised by the multiperiod case.
Several approaches are then proposed:
- one is the direct extension of the axiomatic one-period approach
- two others start from the representation result of the one-period
study, and connection with works by T. Wang and L. Epstein/M. Schneider
is mentionned. The specific extension of Tail Value-at-Risk ("C-VaR")
is examined and discussed.
A paper will be distributed.
(Joint work with F. Delbaen, J.-M. Eber, D. Heath and H. Ku)
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