Abstract
This study examines key characteristics of stock market time series, such as regime shifts and non-linearity, which necessitate specialized methods for capturing market volatility. To improve volatility forecasting for the Muscat Securities Market Index (MSMI), the paper proposes a Back-Propagation Neural Network (BPNN) model. The neural network takes as input the volatility estimated by the Markov-Switching GARCH (MS-GARCH) model and uses the Close-Close volatility estimator as the output. The findings indicate that incorporating the neural network enhances the forecasting accuracy of the MS-GARCH model, as measured by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The main contribution of this study lies in the integration of machine learning with traditional econometric models to improve volatility forecasting. Specifically, it demonstrates that augmenting the MS-GARCH model with a BPNN significantly enhances predictive accuracy, offering a hybrid approach well-suited to capturing the complex dynamics of financial time series.
Keywords
Publication details
- DOI
- 10.53539/2414-536x.1429
- Journal
- Sultan Qaboos University Journal for Science, 31(1), 78-86
- Publisher
- Sultan Qaboos University
- Open access
- Gold open access
- License
- CC BY 4.0
Cite this article
APA 7
Benaid, B., Al Hasani, I., & Eddahbi, M. (2026). Volatility Forecasting for Muscat Security Market Index using Markov Switching GARCH Model and Back-propagation Neural Network. Sultan Qaboos University Journal for Science, 31(1), 78-86. https://doi.org/10.53539/2414-536x.1429
MLA 9
Benaid, Brahim, et al. "Volatility Forecasting for Muscat Security Market Index using Markov Switching GARCH Model and Back-propagation Neural Network." Sultan Qaboos University Journal for Science, vol. 31, no. 1, 2026, pp. 78-86. https://doi.org/10.53539/2414-536x.1429.
Chicago (author–date)
Benaid, Brahim, Iman Al Hasani, and Mhamed Eddahbi. 2026. "Volatility Forecasting for Muscat Security Market Index using Markov Switching GARCH Model and Back-propagation Neural Network." Sultan Qaboos University Journal for Science 31 (1): 78-86. https://doi.org/10.53539/2414-536x.1429.
Harvard
Benaid, B., Al Hasani, I. and Eddahbi, M. (2026) 'Volatility Forecasting for Muscat Security Market Index using Markov Switching GARCH Model and Back-propagation Neural Network', Sultan Qaboos University Journal for Science, 31(1), pp. 78-86. doi:10.53539/2414-536x.1429.
Vancouver
Benaid B, Al Hasani I, Eddahbi M. Volatility Forecasting for Muscat Security Market Index using Markov Switching GARCH Model and Back-propagation Neural Network. Sultan Qaboos University Journal for Science. 2026;31(1):78-86. doi:10.53539/2414-536x.1429
IEEE
B. Benaid, I. Al Hasani, and M. Eddahbi, "Volatility Forecasting for Muscat Security Market Index using Markov Switching GARCH Model and Back-propagation Neural Network," Sultan Qaboos University Journal for Science, vol. 31, no. 1, pp. 78-86, 2026, doi: 10.53539/2414-536x.1429.