ON THE NONLINEAR ESTIMATION OF GARCH MODELS. USING AN EXTENDED KALMAN FILTER
PROCEEDINGS OF THE WORLD CONGRESS ON ENGINEERING
| dc.creator | Bahamonde Rozas, Natalia Carolina | |
| dc.creator | Ossandon Veliz, Sebastian Eduardo | |
| dc.date | 2016-12-27T21:49:13Z | |
| dc.date | 2022-06-17T20:34:29Z | |
| dc.date | 2016-12-27T21:49:13Z | |
| dc.date | 2022-06-17T20:34:29Z | |
| dc.date | 2011 | |
| dc.date.accessioned | 2023-08-22T01:12:06Z | |
| dc.date.available | 2023-08-22T01:12:06Z | |
| dc.identifier | 3080009 | |
| dc.identifier | 9789881821065 | |
| dc.identifier | https://hdl.handle.net/10533/165167 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/8304606 | |
| dc.description | 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. | |
| dc.description | FONDECYT | |
| dc.description | FONDECYT | |
| dc.language | eng | |
| dc.publisher | INTERNATIONAL ASSOCIATION OF ENGINEERS | |
| dc.relation | instname: Conicyt | |
| dc.relation | reponame: Repositorio Digital RI2.0 | |
| dc.relation | instname: Conicyt | |
| dc.relation | reponame: Repositorio Digital RI 2.0 | |
| dc.relation | info:eu-repo/grantAgreement/Fondecyt/3080009 | |
| dc.relation | info:eu-repo/semantics/dataset/hdl.handle.net/10533/93479 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.title | ON THE NONLINEAR ESTIMATION OF GARCH MODELS. USING AN EXTENDED KALMAN FILTER | |
| dc.title | PROCEEDINGS OF THE WORLD CONGRESS ON ENGINEERING | |
| dc.type | Capitulo de libro | |
| dc.type | info:eu-repo/semantics/bookPart |