Understanding Volatility Transmission from Global Commodity Shocks to Frontier Financial Markets: Machine Learning, Nonlinearities, and State Dependence in Kenya

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.