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Forecasting with the Standardized Self-Perturbed Kalman Filter

Grassi, Stefano, Nonejad, Nima, Santucci de Magistris, Paolo (2016) Forecasting with the Standardized Self-Perturbed Kalman Filter. Journal of Applied Econometrics, 32 (2). pp. 318-341. ISSN 0883-7252. E-ISSN 1099-1255. (doi:10.1002/jae.2522) (KAR id:53658)

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http://dx.doi.org/10.1002/jae.2522

Abstract

We propose and study the finite-sample properties of a modified version of the self-perturbed Kalman filter of Park and Jun (1992) for the on-line estimation of models subject to parameter instability. The perturbation term in the updating equation of the state covariance matrix is now weighted by the estimate of the measurement error variance. This avoids the calibration of a design parameter as the perturbation term is scaled by the level of uncertainty in the data. It is shown by Monte Carlo simulations that this perturbation method is associated with a good tracking of the dynamics of the parameters compared to other on-line, classical and Bayesian methods. The standardized self-perturbed Kalman filter is adopted to forecast the equity premium on the S&P 500 index under several model specifications, and determine the extent to which realized volatility

can be used to predict excess returns.

Item Type: Article
DOI/Identification number: 10.1002/jae.2522
Uncontrolled keywords: TVP models, Self-Perturbed Kalman Filter, Forecasting, Equity Premium, Realized Variance
Subjects: H Social Sciences > H Social Sciences (General)
Divisions: Faculties > Social Sciences > School of Economics
Depositing User: Stefano Grassi
Date Deposited: 09 Jan 2016 22:28 UTC
Last Modified: 10 Feb 2020 12:14 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/53658 (The current URI for this page, for reference purposes)
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