Extremes for coherent risk measures

Asimit, A.V. & Li, J. (2016). Extremes for coherent risk measures. Insurance: Mathematics and Economics, 71, pp. 332-341. doi: 10.1016/j.insmatheco.2016.10.003

[img] Text - Accepted Version
Restricted to Repository staff only until 16 April 2018.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (254kB) | Request a copy

Abstract

Various concepts appeared in the existing literature to evaluate the risk exposure of a financial or insurance firm/subsidiary/line of business due to the occurrence of some extreme scenarios. Many of those concepts, such as Marginal Expected Shortfall or Tail Conditional Expectation, are simply some conditional expectations that evaluate the risk in adverse scenarios and are useful for signaling to a decision-maker the poor performance of its risk portfolio or to identify which sub-portfolio is likely to exhibit a massive downside risk. We investigate the latter risk under the assumption that it is measured via a coherent risk measure, which obviously generalizes the idea of only taking the expectation of the downside risk. Multiple examples are given and our numerical illustrations show how the asymptotic approximations can be used in the capital allocation exercise. We have concluded that the expectation of the downside risk does not fairly take into account the individual risk contribution when allocating the VaR-based regulatory capital, and thus, more conservative risk measurements are recommended. Finally, we have found that more conservative risk measurements do not improve the fairness of the cost of capital allocation when the uncertainty with parameter estimation is present, even at a very high level.

Item Type: Article
Uncontrolled Keywords: Capital allocation; Coherent/Distortion risk measure; Conditional tail expectation; Extreme value theory; Marginal expected shortfall; Rapid variation; Regular variation
Subjects: H Social Sciences > HF Commerce
H Social Sciences > HG Finance
Divisions: Cass Business School > Faculty of Actuarial Science & Insurance
Related URLs:
URI: http://openaccess.city.ac.uk/id/eprint/16268

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics