Abstract:
This study models volatility in the daily Nigeria Stock Exchange 30 index from Jan- uary 30, 2012 to August 31, 2020, comparing the Modified Iterated Cumulative Sums of Squares (MICSS) and the traditional cumulative sum of squares (CUSUMSQ) ap- proaches. Findings reveal that integrating structural breaks reduces volatility persis- tence, and the MICSS outperformed the CUSUM and CUSUMSQ algorithms. Also, incorporating structural breaks reduces the impact of good and bad news on volatil- ity. The study emphasizes the importance of adopting a robust methodology, such as the MICSS, in detecting structural breaks when estimating conditional variances, as incorporating structural breaks in the estimation is pivotal for model robustness, avoiding biases, and improving the estimation of volatility dynamics in financial time series. For investors and risk managers, the practical implications of these findings are: incorporating these breaks enhances risk assessment, portfolio allocation, and hedging strategies. For policymakers, incorporating structural breaks can lead to a more informed regulatory framework that helps stabilize the financial markets.