Abstract
In this paper centered convex previsions are introduced as a special class of imprecise previsions, showing that they retain or generalise most of the relevant properties of coherent imprecise previsions but are not necessarily positively homogeneous. The broader class of convex imprecise previsions is also studied and its fundamental properties are demonstrated, introducing in particular a notion of convex natural extension which parallels that of natural extension but has a larger domain of applicability. These concepts appear to have potentially many applications. In this paper they are applied to risk measurement, leading to a general definition of convex risk measure which corresponds, when its domain is a linear space, to the one recently introduced in risk measurement literature.
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Pelessoni, R., Vicig, P. Convex Imprecise Previsions. Reliable Computing 9, 465–485 (2003). https://doi.org/10.1023/A:1025870204905
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DOI: https://doi.org/10.1023/A:1025870204905