Based on Boolean-valued models we provide a method to interpret a theorem of representation of convex risk measures as a theorem for conditional risk measures which also holds thanks to transfer principle of Boolean-valued models. In particular, we establish a general robust representation theorem for conditional risk measures.
On conditional Lebesgue property for conditional risk measures
On robust representation of conditional risk measures on a $L^\infty$-type module
A note on conditional risk measures of Orlicz spaces and Orlicz-type modules
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