Probabilistic Gradient Boosting with Student-t Output Layer and LIME Explanations for Exchange Rate Forecasting: Evidence from Ghana

This paper develops a Probabilistic Gradient Boosting model with a Student-t output layer (St-NGBoost) for density forecasting of Ghana Cedi exchange rates against the US Dollar, British Pound, and Euro, augmented with Local Interpretable Model-agnostic Explanations (LIME) for transparency. Using 221 monthly log-return observations from January 2007 to June 2025, alongside eleven macroeconomic covariates including inflation, money supply M2, interest rates, oil, gold, and cocoa prices. It was demonstrated that the log-returns exhibit extreme leptokurtosis (excess kurtosis 9.55–14.65) and significant negative skewness, making the Gaussian distributional assumption untenable. St-NGBoost parameterizes the predictive density as a Student-t distribution whose location, scale, and degrees-of-freedom parameters are the outputs of a gradient-boosted ensemble, trained by minimizing the natural gradient of the negative log-likelihood. On a held-out test set spanning the high-volatility post-COVID and 2022 hyperinflationary episode, St-NGBoost delivers AIC improvements of 101.7–284.8 units over the Gaussian NGBoost benchmark, Continuous Ranked Probability Score (CRPS) reductions of 28–39%, and empirical 90% prediction interval coverage within 1.5 percentage points of the nominal level. Global LIME explanations identify inflation (11.3%), oil price (10.3%), GDP growth (9.8%), and M2 growth (8.9%) as the dominant predictive features. Local LIME explanations further reveal that the model correctly shifts explanatory weight toward inflation and interest-rate variables during the 2022 crisis months, providing economically coherent and actionable interpretations for policy-makers.