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.

Aim: The Kenyan financial market is weakly inefficient due to calendar anomalies explained by stock returns volatility. However, there is a dearth of literature on how the results may vary when different models are applied especially in Africa. Therefore, this study attempted to establish the calendar anomalies in the stock returns volatility using OLS and GARCH (1,1) models. Methods: A descriptive research design was adopted. The daily closing prices data for NSE 20 share index between January 1994 and December 2014 with a total of 5203 observations was used. Results: The results suggest that, the day of the week effect is significant in both models where Friday had the highest returns while Monday had the lowest returns but, the January effect is only explained in the OLS model which disappears in the GARCH (1,1). Conclusion: The coefficients of the volatility equation for GARCH (1,1) are positive, significant and their summation is close to one indicating that the volatility is persistent at the NSE. The study however applied OLS and GARCH (1,1) which have limitations. Recommendation: The study recommend the use of other GARCH models to determine if the findings are robust to different models. JEL Classification: G11; G12; C31; C22

Aim: The purpose of this study was to analyze the mediating role of foreign direct investment (FDI) in the relationship between the selected socio-political factors and economic growth in Kenya, Uganda and Tanzania. Methods: A descriptive research design was adopted. The secondary data for the three countries for 41 years from 1975 to 2015 collected from World Bank database was utilized and multi-group path analysis was applied. Results: The results suggest that human capital development is the main determinant of FDIs with 66% direct effects, 17% indirect effects through FDI and a total effect of 83% on economic growth in the East African region. While ease of doing business and corruption perception together, ceteris paribus, account for only 9.8% of the total changes in the economic growth in the region. Conclusion: Foreign direct investment was a significant mediator in the relationship between the three social-political factors and economic growth in the three east African countries. Recommendation: The study recommends that the three East African countries should adopt simpler and better regulations for businesses to make it easier for foreign as well local investors to enter business and trade locally and internationally.

Frontier financial markets face a persistent diagnostic gap in forecasting volatility: traditional linear and single regime GARCH models fail to capture structural breaks, nonlinear spillovers, and regime transitions that define fragile market dynamics. In particular, ARMA–FIGARCH specifications could not exhaust residual linear persistence, motivating the integration of Long Short Term Memory (LSTM) networks to close this gap. This limitation leaves policymakers without timely, actionable signals to anticipate systemic stress. To address this gap, this study developed a hybrid Long Short Term Memory (LSTM) neural network combined with Fractionally Integrated GARCH (FIGARCH) econometric filtering to design a regime sensitive early warning system (EWS) in a frontier financial market. Using daily data from the NSE 20 Share Index, USD/KES exchange rate, and Brent crude volatility spanning 2000–2025 forecasting horizons were first assessed, showing that one day signals were overly reactive and ten day forecasts diluted precision, while five day predictions achieved the strongest balance with 98% accuracy and robust detection across Calm (F1 = 0.99), Moderate (F1 = 0.82), and Stress (F1 = 0.69) regimes. Econometric validation confirmed that conditional heteroskedasticity was adequately captured by the FIGARCH specification, though residual autocorrelation required AR refinements to improve model fit. Building on these diagnostics, probabilistic stress thresholds were developed to translate regime signals into actionable alarms, distinguishing transitional phases from full stress episodes and providing regulators with a practical buffer for intervention. The hybrid model thus advances methodological innovation by combining long memory volatility modelling with deep learning adaptability, while delivering Basel-compliant tail-risk alarms and a state-dependent policy matrix for regulators, offering policy relevant tools for monitoring volatility and crisis preparedness. The findings underscore the importance of regime sensitive approaches in frontier markets, offering credible and timely signals to anticipate and mitigate systemic stress, and contributing to both academic literature and practical financial stability oversight.

Financial development is widely regarded as a driver of economic development in Africa, yet the conditioning role of institutional quality has not been systematically synthesised. This systematic review, conducted in accordance with PRISMA 2020 guidelines, examines 94 peer-reviewed studies on the relationship between financial development, institutional quality, and economic development in African and comparable emerging-market contexts. Ninety-two of these studies provide direct empirical evidence, with 89 falling within a formal 2007–2025 search window, and three seminal studies from 2005 to 2006 purposively retained outside this window for theoretical grounding. The remaining two studies are prior reviews, retained solely to position this review against earlier work. Eligible studies were identified through a structured search of six databases and screened using PICOS-based criteria adapted for the econometric literature. Methodological quality was appraised using the Mixed Methods Appraisal Tool. The review’s primary contribution is a three-mechanism synthesis of amplification, threshold, and substitution, through which institutional quality moderates the growth and development effects of financial development. Institutions, in turn, independently shape financial development itself. Building on a gap identified within this synthesis, the review proposes, as a secondary and empirically untested extension, a FinTech Mediation Hypothesis. Financial technology may reduce transaction costs and information frictions along a pathway conditioned by institutional quality. The findings imply that institutional reform and financial-sector development should be pursued jointly, with FinTech regulatory frameworks designed to exploit this complementarity while safeguarding financial stability and consumer protection.

Global commodity shocks are associated with volatility in frontier financial markets, affecting exchange rates and equity indices. 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 regimeindependent dynamics. Empirical evidence demonstrates that 2008 Global Financial Crisis and COVID19 induced permanent volatility regime changes. This study examined volatility transmission from global commodity shocks to a frontier financial market, focusing on the USD/KES exchange rate 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 longmemory dynamics, with limited evidence of leverage effects. Breakadjusted models improve inference by correcting spurious persistence, while machine learning approaches demonstrate superior tracking of volatility during stress regimes. We show that volatility transmission from global commodity shocks to a frontier market is nonlinear, break-sensitive, and state-dependent, and that hybrid ML-econometric methods improve forecasting during crisis-period. Findings highlight persistence distortion, horizondependent performance, and relevance of regimesensitive modelling frameworks for financial stability in structurally evolving economies.