On the nonlinear estimation of GARCH models using an Extended Kalman Filter

Sebastián Ossandón, NATALIA BAHAMONDE ROZAS

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the World Congress on Engineering 2011, WCE 2011
Pages148-151
Number of pages4
StatePublished - 14 Nov 2011
EventWorld Congress on Engineering 2011, WCE 2011 - London, United Kingdom
Duration: 6 Jul 20118 Jul 2011

Publication series

NameProceedings of the World Congress on Engineering 2011, WCE 2011
Volume1

Conference

ConferenceWorld Congress on Engineering 2011, WCE 2011
CountryUnited Kingdom
CityLondon
Period6/07/118/07/11

Keywords

  • Discrete-time nonlinear state space model
  • Extended Kalman Filter
  • GARCH models
  • Nonlinear parameter estimation
  • Nonlinear state estimation

Fingerprint Dive into the research topics of 'On the nonlinear estimation of GARCH models using an Extended Kalman Filter'. Together they form a unique fingerprint.

Cite this