Estimation and prediction of time-varying GARCH models through a state-space representation: a computational approach. Issue 12 (13th August 2017)

• Record Type: Journal Article
• Authors: Ferreira, Guillermo; Navarrete, Jean P.; Rodríguez-Cortés, Francisco J.; Mateu, Jorge
• Is Part Of: Journal of statistical computation and simulation. Volume 87:Issue 12(2017)
• Page Start: 2430
• Page End: 2449
• Publication Date: 2017-08-13
• Subjects: GARCH models -- local stationarity -- long-range dependence -- state-space representation -- time-varying models; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2017.1334778
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: We propose a state-space approach for GARCH models with time-varying parameters able to deal with non-stationarity that is usually observed in a wide variety of time series. The parameters of the non-stationary model are allowed to vary smoothly over time through non-negative deterministic functions. We implement the estimation of the time-varying parameters in the time domain through Kalman filter recursive equations, finding a state-space representation of a class of time-varying GARCH models. We provide prediction intervals for time-varying GARCH models and, additionally, we propose a simple methodology for handling missing values. Finally, the proposed methodology is applied to the Chilean Stock Market (IPSA) and to the American Standard&Poor's 500 index (S&P500).