Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/105701
Authors: 
Grassi, Stefano
Nonejad, Nima
Santucci de Magistris, Paolo
Year of Publication: 
2014
Series/Report no.: 
School of Economics Discussion Papers 1405
Abstract: 
A modification of the self-perturbed Kalman filter of Park and Jun (1992) is proposed for the on-line estimation of models subject to parameter instability. The perturbation term in the updating equation of the state covariance matrix is weighted by the measurement error variance, thus avoiding the calibration of a design parameter. The standardization leads to a better tracking of the dynamics of the parameters compared to other on-line methods, especially as the level of noise increases. The proposed estimation method, coupled with dynamic model averaging and selection, is adopted to forecast S&P 500 realized volatility series with a time-varying parameters HAR model with exogenous variables.
Subjects: 
TVP models
Self-Perturbed Kalman Filter
Dynamic Model Averaging
Dynamic Model Selection
Forecasting
Realized Variance
JEL: 
C10
C11
C22
C80
Document Type: 
Working Paper

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