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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.

Volatility Estimation of Financial Returns Using Risk-Sensitive Particle Filters