• Communications for Statistical Applications and Methods
• /
• v.31 no.5
• /
• pp.535-556
• /
• 2024

Measuring stock market volatility and its determinants is critical for stock market participants, as volatility spillover effects affect corporate performance. This study adopted a novel approach to analysing and implementing GARCH-MIDAS modelling methods. The classical GARCH as a benchmark and the univariate GARCH-MIDAS framework are the GARCH family models whose forecasting outcomes are examined. The outcome of GARCH-MIDAS analyses suggests that inflation, interest rate, exchange rate, and oil price are significant determinants of the volatility of the Johannesburg Stock Market All Share Index. While for Nigeria, the volatility reacts significantly to the exchange rate and oil price. Furthermore, inflation, exchange rate, interest rate, and oil price significantly influence Ghanaian equity volatility, especially for the long-term volatility component. The significant shock of the oil price and exchange rate to volatility is present in all three markets using the generalized autoregressive conditional heteroscedastic-mixed data sampling (GARCH-MIDAS) framework. The GARCH-MIDAS, with a powerful fusion of the GARCH model's volatility-capturing capabilities and the MIDAS approach's ability to handle mixed-frequency data, predicts the volatility for all variables better than the traditional GARCH framework. Incorporating these two techniques provides an innovative and comprehensive approach to modelling stock returns, making it an extremely useful tool for researchers, financial analysts, and investors.

• The Journal of the Korea Contents Association
• /
• v.15 no.8
• /
• pp.460-472
• /
• 2015

The aim of this study is to analyze the Weekend and January effect in the Ghana Stock Exchange (GSE) using daily closing prices of GSE-All Share Index (ASI) and Composite Index (CI) between the period of January 4th, 2005 and December 31st, 2013. The dataset covers the period of 2005 to 2010 (6 years) for the ASI and 2011 to 2013 (3 years) for the CI. The following results are obtained based on a parametric regression using dummy variables. First, no weekly effect or anomaly is documented for both GSE-ASI and GSE-CI. Second, market abnormalities are captured for both GSE-ASI and GSE-CI over their respective entire periods. However, no consistent April effect is found for ASI when the period was segregated into two periods of three years. The April effect is uncovered for the GSE-ASI at 5% significant level while the January effect is found for the GSE-CI at 1% significant level.