Support Vector Regression for Forecasting Ghana’s Usd to Ghs Exchange Rate_A Comparative Study With Econometric and ML/QML Baselines.

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dc.contributor.authorAnhwere, K.L.
dc.date.accessioned2026-03-02T13:01:36Z
dc.date.available2026-03-02T13:01:36Z
dc.date.issued2025-11
dc.description.abstractGhana continues to struggle with accurate exchange-rate forecasting because to structural breaks, nonlinear macroeconomic dynamics, and the shortcomings of conventional econometric models like ARIMA, ARIMAX, and VAR. These Bank of Ghana institutional workhorses are predicated on linearity, stationarity, and steady parameters—conditions that are increasingly broken in the fluctuating USD/GHS market. Studies that have already been conducted in Ghana frequently benchmark forecasting models inconsistently, lack unified assessment pipelines, and fail to evaluate operational usability or computational efficiency. The question of whether contemporary machine-learning approaches can produce much better and more trustworthy forecasts for decision-making is thus left open from a methodological and policy standpoint. This study investigates whether a kernel-based Support Vector Regression (SVR) model provides better operational robustness and prediction accuracy than the baseline models used by the Bank of Ghana. We employ a two-phase strategy using a monthly macroeconomic panel spanning 2014– 2023: (i) an ex-post backtest (train 2014–2022; test 2023) and (ii) an ex-ante operational forecast for January–December 2024 following model refitting on data through December 2023. Using accuracy metrics (MAE, RMSE, MSE, MAPE, R²), computational cost indicators (training time, inference time, peak memory), and uncertainty quantification (native intervals for econometric models, bootstrap intervals for ML/QML), all models, econometric, classical ML, and exploratory quantum ML, are assessed within a single, leakage-safe pipeline. According to the 2023 backtest results, SVR significantly outperforms ARIMA and VAR, although ARIMAX is still the most effective conventional comparator. While quantum variations show promise but inconsistent accuracy, classical machine learning methods (LSTM, XGBoost) offer moderate increases. A policy-ready Streamlit prototype that operationalizes model selection, scenario analysis, uncertainty reporting, and exportable outputs for institutional usage is shown in the study's conclusion.
dc.identifier.urihttps://space.uenr.edu.gh//handle/123456789/61
dc.language.isoen
dc.publisherUENR
dc.subjectQuantum Machine Learning
dc.subjectClassical Algorithms
dc.subjectMicro-Economic Forecasting
dc.titleSupport Vector Regression for Forecasting Ghana’s Usd to Ghs Exchange Rate_A Comparative Study With Econometric and ML/QML Baselines.
dc.typeThesis

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