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MODELLING MEAN AND VOLATILITY OF CEMENT STOCKS: A CASE STUDY OF SAUDI CEMENT COMPANY
Keywords:
forecasting, stationarity, autocorrelation, diagnostics, unit-root test, information criteria.DOI:
https://doi.org/10.17654/0972361724008Abstract
Present study focuses on forecasting the stock prices for Saudi Cement Company using time series tools. Data for a period of one year closing stock prices (15-09-2022 to 14-09-2023) for a total of 248 days were downloaded using Yahoo.finance.com. For modelling mean, autoregressive integrated moving average (ARIMA) model was used and for modelling volatility, autoregressive conditional heteroscedasticity (ARCH) was used. Mathematical results were derived by using R software and EViews to elicit the desired information. The results were supplemented by relevant tables and graphs. Moreover, the results of the study showed that ARIMA(0, 1, 1) was the most appropriate model for Saudi Cement Company for prediction purposes. Using LM test, ARCH effects were not found in the series. Outcomes of the present study will be two pronged – first it will bridge the gap between the academia and practitioners as easy-to-follow approach, for eliciting results which were adopted, secondly the outcomes will provide guidelines for the prospective investors on when and why to invest in the stocks of cement industry within the Saudian context.
Received: October 7, 2023
Accepted: November 28, 2023
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