Estimation and prediction of time-varying GARCH models through a state-space representation: a computational approach

被引:6
作者
Ferreira, Guillermo [1 ]
Navarrete, Jean P. [2 ]
Rodriguez-Cortes, Francisco J. [3 ]
Mateu, Jorge [3 ]
机构
[1] Univ Concepcion, Dept Stat, Concepcion, Chile
[2] Univ Milano Bicocca, Dept Stat & Quantitat Methods, Milan, Italy
[3] Univ Jaume 1, Dept Math, Castellon de La Plana, Spain
关键词
GARCH models; local stationarity; long-range dependence; state-space representation; time-varying models; BOOTSTRAP PREDICTION; FORECAST INTERVALS; KALMAN FILTER; ARCH; VOLATILITY; VARIANCE; SERIES;
D O I
10.1080/00949655.2017.1334778
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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).
引用
收藏
页码:2430 / 2449
页数:20
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