Abstract
This study aimed at modeling and forecasting volatility of the Standard Bank Stock Prices using GARCH and ARIMA models. ARIMA is the Autoregressive Integrated Moving Average and GARCH represents Generalized AutoRegressive Conditional Heteroskedasticity. The time series data used in the study is from February 2006 to July 2022 and comprises of 198 observations downloaded from Yahoo finance. The best model was selected using the Akaike Information Criterion (AIC) procedure. The model with the smallest AIC was selected as the best model. GARCH (1,1)-ARMA (2,2) was the best model for modeling and forecasting volatility in the Standard Bank stock prices. The selected model were used to forecast the Standard Bank monthly closing stock prices. The results obtained revealed a gradual increase in Standard Bank closing stock prices in the next year.
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Shogole, L.M.P., Nwanamidwa, S., Chanza, M., Munapo, E., Mpeta, K.N. (2023). Modeling and Forecasting Bank Stock Prices: GARCH and ARIMA Approaches. In: Vasant, P., et al. Intelligent Computing and Optimization. ICO 2023. Lecture Notes in Networks and Systems, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-031-50151-7_12
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