An innovative approach to financial market analysis: hybrid ARFIMA with Sieve and Moving Block Bootstrap - Alshaimaa Elwasify,; Zaidi Isa, (Penerbit Universiti Kebangsaan Malaysia, 2025)
This paper aims to develop the field of financial time series analysis by focusing on the Egyptian stock market, EGX 30 in particular, using innovative modeling and forecasting techniques. Our study explores the application of ARFIMA models either independently or in combination with advanced bootstrap techniques to improve the accuracy of parameter estimation and forecasting. The study includes four main methodologies: the traditional ARFIMA model, ARFIMA with Sieve Bootstrap (SB), ARFIMA with Moving Block Bootstrap (MBB), and the proposed model that combines the two bootstrap techniques with the ARFIMA model. The proposed model aims to address the time complexities in the financial series, including long term and short-term dependencies. The results show that the proposed model significantly outperforms other traditional and combined models in terms of forecasting accuracy and estimation reliability. This improved performance underscores the importance of integrating advanced bootstrap techniques with traditional models to better understand the complex characteristics of financial data. Our paper contributes to scientific literature by introducing a new approach that has not been applied before in financial markets. It also offers practical applications for investors and financial analysts by providing a robust framework for forecasting and supporting decision-making in dynamic and volatile market environments, with a focus on the Egyptian market. This study represents a basis for applying similar methodologies in other emerging markets.