Karel MUNDNICH, Marcos E. ORCHARD, Jorge F. SILVA, Patricio PARADA
Electrical Engineering Department, Universidad de Chile
Av. Tupper 2007, Santiago, 8370451, CHILE
Abstract: 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.
Keywords: Bayesian estimation, stochastic volatility, particle filters, entropy.
CITE THIS PAPER AS:
Karel MUNDNICH, Marcos E. ORCHARD, Jorge F. SILVA, Patricio PARADA, Volatility Estimation of Financial Returns Using Risk-Sensitive Particle Filters, Studies in Informatics and Control, ISSN 1220-1766, vol. 22 (3), pp. 297-306, 2013.