Pesenti, S. M., Millossovich, P. & Tsanakas, A. (2016). Robustness Regions for Measures of Risk Aggregation. Dependence Modeling, 4(1), pp. 348367. doi: 10.1515/demo20160020

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Abstract
One of risk measures key purposes is to consistently rank and distinguish between different risk profiles. From a practical perspective, a risk measure should also be robust, that is, insensitive to small perturbations in input assumptions. It is known in the literature [14, 39], that strong assumptions on the risk measures ability to distinguish between risks may lead to a lack of robustness. We address the tradeoff between robustness and consistent risk ranking by specifying the regions in the space of distribution functions, where lawinvariant convex risk measures are indeed robust. Examples include the set of random variables with bounded second moment and those that are less volatile (in convex order) than random variables in a given uniformly integrable set. Typically, a risk measure is evaluated on the output of an aggregation function defined on a set of random input vectors. Extending the definition of robustness to this setting, we find that lawinvariant convex risk measures are robust for any aggregation function that satisfies a linear growth condition in the tail, provided that the set of possible marginals is uniformly integrable. Thus, we obtain that all lawinvariant convex risk measures possess the aggregationrobustness property introduced by [26] further studied by [40]. This is in contrast to the widelyused, nonconvex, risk measure ValueatRisk, whose robustness in a risk aggregation context requires restricting the possible dependence structures of the input vectors.
Item Type:  Article 

Uncontrolled Keywords:  Convex risk measures, Aggregation, ValueatRisk, Robustness, Continuity 
Subjects:  H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management 
Divisions:  Cass Business School > Faculty of Actuarial Science & Insurance 
URI:  http://openaccess.city.ac.uk/id/eprint/15931 
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