Quantitative Easing: Money Supply and Commodity Prices of Oil, Gold, and Cocoa in Ghana

This study investigates the dynamic relationships between the money supply (M2) and key commodity prices (Cocoa, Gold, and Crude) in the context of Ghana. Utilizing Vector Error Correction Model (VECM) analysis, we analyze the short-term and long-term Granger causality relationships among these variables, aiming to shed light on the potential linkages between monetary policy and commodity markets. The analysis covers the period from December 1999 to April 2023, using lag structures of 1 and 8 to capture both short-term and more enduring effects. Our findings reveal significant Granger causality relationships between the money supply and various commodities, with nuanced patterns emerging across different lags. In the short-run, our results suggest bidirectional causal relationships between COCOA and M2, CRUDE and M2, and GOLD and M2. Additionally, M2 Granger causes changes in COCOA, CRUDE, and GOLD. However, the causal relationship between COCOA and GOLD appears to be unidirectional, with COCOA not significantly Granger causing changes in GOLD. The short-term findings highlight the intricate interplay between monetary policy and commodity markets. In the long-run (lag 8), our analysis unveils robust Granger causality relationships between the variables. Past values of COCOA, CRUDE, and GOLD Granger cause changes in M2, indicating a notable influence of commodity markets on the money supply. Similarly, M2 Granger causes changes in CRUDE and GOLD. Notably, the findings underscore a more comprehensive and intertwined relationship between monetary policy and commodity prices in the long-run. Based on these results, we derive several policy implications. Policymakers should carefully consider the potential impact of monetary policy decisions, such as quantitative easing, on commodity markets and price dynamics. Measures to stabilize commodity prices, promote export diversification, manage inflation expectations, and enhance economic resilience are recommended. Additionally, effective data monitoring, international collaboration, and proactive risk management strategies are essential components for navigating the complex interactions