TY - GEN
T1 - On the nonlinear estimation of GARCH models using an Extended Kalman Filter
AU - Ossandón, Sebastián
AU - Bahamonde, Natalia
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Discrete-time nonlinear state space model
KW - Extended Kalman Filter
KW - GARCH models
KW - Nonlinear parameter estimation
KW - Nonlinear state estimation
UR - http://www.scopus.com/inward/record.url?scp=80755127039&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:80755127039
SN - 9789881821065
T3 - Proceedings of the World Congress on Engineering 2011, WCE 2011
SP - 148
EP - 151
BT - Proceedings of the World Congress on Engineering 2011, WCE 2011
T2 - World Congress on Engineering 2011, WCE 2011
Y2 - 6 July 2011 through 8 July 2011
ER -