On the nonlinear estimation of GARCH models using an extended Kalman filter

Resumen

A new mathematical representation, based on a discrete-time nonlinear state space formulation, is presented to characterize a Generalized Auto Regresive Conditional Heteroskedasticity (GARCH) model. Nonlinear parameter estimation and nonlinear state estimation, for this state space model, using an Extended Kalman Filter (EKF) are described. Finally some numerical results, which make evident the effectiveness and relevance of the proposed nonlinear estimation are given.

Autores: Ossandón, S., Bahamonde, N.

Journal: Proceedings of the World Congress on Engineering 2011

Journal Volume: 1

Journal Issue:

Journal Page: 148-151

Tipo de publicación: Scopus

Fecha de publicación: 2011

Topics: Discrete-time nonlinear state space model, Extended Kalman Filter, GARCH models, Nonlinear parameter estimation, Nonlinear state estimation

DOI:

URL de la publicación: https://pdfs.semanticscholar.org/dbae/8b2113dec55a4a70cd46b10fbd0f3ec670b5.pdf