Enhancing time series forecasting : applying cross-validation methods to hybrid models for predicting GCC stock market indices

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Alanazi, Kamel and Gray, Alison (2026) Enhancing time series forecasting : applying cross-validation methods to hybrid models for predicting GCC stock market indices. Journal of Risk and Financial Management, 19 (9). 699. ISSN 1911-8066 (https://doi.org/10.3390/jrfm19090699)

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Abstract

Forecasting Gulf Cooperation Council (GCC) country stock market returns is challenging because these markets exhibit volatility clustering, heavy tails, oil-price sensitivity, and asymmetric responses to shocks. This study evaluates whether ARIMA–GARCH-family hybrid models improve one-step-ahead forecasting of daily GCC stock index returns relative to single econometric specifications and whether time-series cross-validation provides a more robust model-selection framework than relying solely on information criteria such as AIC. Daily index data for Saudi Arabia, Kuwait, Bahrain, Qatar, Oman, Abu Dhabi, and Dubai from 3 October 2012 to 3 November 2022 are modelled using ARIMA, GARCH, TGARCH, APARCH, and hybrid ARIMA–GARCH-family models with Student-t innovations. Forecasting performance is assessed using RMSE, MAE, and MAPE measures under holdout testing, block cross-validation, walk-forward cross-validation, and rolling-window cross-validation. Model differences are evaluated using Friedman and Diebold–Mariano tests. The results show that single ARIMA specifications do not adequately capture the conditional heteroscedasticity and heavy-tailed behaviour of GCC stock index returns. Hybrid ARIMA–GARCH-family models provide more stable forecasting performance, with ARIMA–TGARCH(1,1)-t specifications selected for most GCC markets. ARIMA(5,0,3)–APARCH(1,1)-t provides the strongest performance for the Saudi index. The findings show that time-series cross-validation can reduce the selection of potentially over-complex models indicated by the AIC criterion and provides a more reliable basis for selecting forecasting models in emerging, oil-dependent financial markets.

ORCID iDs

Alanazi, Kamel and Gray, Alison ORCID logoORCID: https://orcid.org/0000-0002-6273-0637;
CORE (COnnecting REpositories)