  Our paper contributes to the theory of conditional risk measures and
conditional certainty equivalents. We adopt a random modular approach which
proved to be effective in the study of modular convex analysis and conditional
risk measures. In particular, we study the conditional counterpart of optimized
certainty equivalents. In the process, we provide representation results for
niveloids in the conditional $L^{\infty}$-space. By employing such
representation results we retrieve a conditional version of the variational
formula for optimized certainty equivalents. In conclusion, we apply this
formula to provide a variational representation of the conditional entropic
risk measure.
