This article presents and analyzes the implementation of risk-sensitive particle filtering algorithm for volatility estimation of continuously compounded returns of financial assets. The proposed approach uses a stochastic state-space representation for the evolution of the dynamic system -the unobserved generalized autoregressive conditional heteroskedasticity (uGARCH)model- and an Inverse Gamma distribution as risk functional (and importance density distribution) to ensure the allocation of particles in regions of the state-space that are associated to sudden changes in the volatility of the system. A set of ad-hoc performance and entropy-based measures is used to compare the performance of this scheme with respect to a classic implementation of sequential Monte Carlo methods, both in terms of accuracy and precision of the resulting volatility estimates; considering for this purpose data sets generated in a blind-test format with GARCH structures and time-varying parameters.