This paper introduces a theoretical framework for accelerating the evaluation of Conditional Value-at-Risk (CVaR) value functions in Partially Observable Markov Decision Processes (POMDPs) with formal performance guarantees. It derives novel CVaR bounds for random variables, enabling faster policy evaluation through action elimination using simplified models.