Capitulo de libro
ON THE NONLINEAR ESTIMATION OF GARCH MODELS. USING AN EXTENDED KALMAN FILTER
PROCEEDINGS OF THE WORLD CONGRESS ON ENGINEERING
Registro en:
3080009
9789881821065
Autor
Bahamonde Rozas, Natalia Carolina
Ossandon Veliz, Sebastian Eduardo
Institución
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.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.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.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.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.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.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.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.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.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.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.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.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.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.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. FONDECYT FONDECYT