The framework presents a method to quantify #uncertainty propagation in #dynamic #scenarios, focusing on discrete #stochastic processes over a limited time span. These dynamic uncertainty sets encompass various uncertainties like distributional ambiguity, utilizing tools like the Wasserstein distance and $f$-divergences. Dynamic robust #risk #measures, defined as maximum #risks within uncertainty sets, exhibit properties like convexity and coherence based on uncertainty set conditions. $f$-divergence-derived sets yield strong time-consistency, while Wasserstein distance leads to a new non-normalized time-consistency. Recursive representations of one-step conditional robust risk measures underlie strong or non-normalized time-consistency.
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