Modeling and Forecasting Historical Volatility Using Econometric and Deep Learning Approaches: Evidence from the Moroccan and Bahraini Stock Markets

Imane Boudri () and Abdelhamid El Bouhadi
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Imane Boudri: National School of Business and Management, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco
Abdelhamid El Bouhadi: National School of Business and Management, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco

JRFM, 2024, vol. 17, issue 7, 1-15

Abstract: This study challenges the prevailing belief in the necessity of complex models for accurate forecasting by demonstrating the effectiveness of parsimonious econometric models, namely ARCH(1) and GARCH(1,1), over deep learning robust approaches, such as LSTM and 1D-CNN neural networks, in modeling historical volatility within pre-emerging stock markets, specifically the Moroccan and Bahraini stock markets. The findings suggest reevaluating the balance between model complexity and predictive accuracy. Future research directions include investigating the potential existence of threshold effects in market capitalization for optimal model performance. This research contributes to a deeper understanding of volatility dynamics and enhances forecasting models’ effectiveness in diverse market conditions.

Keywords: historical volatility; pre-emerging markets; ARCH-GARCH models; deep learning approaches; LSTM network; 1D-CNN network (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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