Journal Articles
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Item Understanding Volatility Transmission from Global Commodity Shocks to Frontier Financial Markets: Machine Learning, Nonlinearities, and State Dependence in Kenya(Journal of Risk and Financial Management, 2026) Simiyu, Christine N.; Laiboni, Munene; Wawire, Abraham K.Global commodity shocks are associated with volatility in frontier financial markets, affecting exchange rates and equity indices. This study examined volatility transmission from global commodity shocks to Kenya’s USD/KES exchange rate and the NSE 20 Share Index using daily data from November 1997 to December 2024. While GARCH specifications capture clustering, they are sensitive to structural breaks and regime changes, which distort persistence and weaken risk measures. Machine learning approaches provide alternatives capable of capturing nonlinear dependencies, abrupt volatility bursts, and regime-independent dynamics. Empirical evidence demonstrates that the 2008 Global Financial Crisis and COVID-19 induced permanent volatility regime changes. This study examined volatility transmission from global commodity shocks to a frontier financial market, focusing on the USD/KES and the NSE 20 Share Index. Structural break-detection was integrated through the Iterative Cumulative Sum of Squares algorithm, alongside APARCH, FIGARCH models and ML architectures (XGBoost, LSTM). In Kenya volatility is characterized by strong persistence and long-memory dynamics, with limited evidence of leverage effects. Break-adjusted models improve inference by correcting spurious persistence, while machine learning approaches demonstrate superior tracking of volatility during stress regimes. Volatility transmission is nonlinear, break-sensitive, and state-dependent; hybrid ML–econometric methods enhance crisis forecasting and regime-sensitive financial stability analysis.Item Applying Data Mining in Graduates’ Employability : A Systematic Literature Review(International Journal of Engineering Pedagogy, 2023) Mburu, Lucy W.; Mwendia, Simon N.; Mpia, Héritier N.Envisaging an adequate IT/IS solution that can mitigate the employability problems is imperative because nowadays there is a high rate of unemployed graduates. Thus, the main goal of this systematic literature review (SLR) was to explore the application of data mining techniques in modeling employability and see how those techniques have been applied and which factors/variables have been retained to be the most predictors or/and prescribers of employability. Data mining techniques have shown the ability to serve as decision support tools in predicting and even prescribing employability. The review determined and analyzed the machine learning algorithms used in data mining to either predict or prescribe employability. This review used the PRISMA method to determine which studies from the existing literature to include as items for this SLR. Hence, 20 relevant studies, 16 of which are predicting employability and 4 of which are prescribing employability. These studies were selected from reliable databases: ScienceDirect, Springer, Wiley, IEEE Xplore, and Taylor and Francis. According to the results of this study, various data mining techniques can be used to predict and/or to prescribe employability. Furthermore, the variables/factors that predict and prescribe employability vary by country and the type of prediction or prescription conducted research. Nevertheless, all previous studies have relied more on skill as the main factor that predict and/or prescribe employability in developed countries and none studies have been conducted in unstable developing countries. Therefore, the need to conduct research on predicting or prescribing employability in such countries by trying to use contextual factors beyond skill as features.