Title: Robustifying Forecast Performance Through Hybridized Arima-Garch-Type Modeling in a Discrete-Time Stochastic Series
Author(s): Imoh Udo Moffat, Emmanuel Alphonsus Akpan
Pages: 559-571
Cite as:
Imoh Udo Moffat, Emmanuel Alphonsus Akpan, Robustifying Forecast Performance Through Hybridized Arima-Garch-Type Modeling in a Discrete-Time Stochastic Series, Int. J. Anal. Appl., 18 (4) (2020), 559-571.

Abstract


The study is aimed at investigating the robustness of forecast performance of a hybridized (ARIMA-GARCH-type) model over each single component using different periods of horizon to display consistency over time. Daily closing share prices were explored from the Nigerian Stock Exchange for First City Monument Bank and Wema Bank Plc, spanning from January 3, 2006 to December 30, 2016, with a total of 2,713 observations. ARIMA model, GARCH-type, and hybridized ARIMA-GARCH-type were considered. The hybridized ARIMA-GARCH-type was found to produce the best forecast performance in terms of robustness over each single component model and the robustness was found to be consistent over different time horizons for the datasets. The implication is that, it provides an essential remedy to the problem associated with model instability when forecasting a discrete-time stochastic series.

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