Analyzing the Relationship Between Global Commodity Price Volatility and Exchange Rate Fluctuation Dynamics

Posted: Sep 21, 2023

• Theodore Rodriguez
• Maria Anderson
• Owen Allen

Abstract

This research introduces a novel computational framework that integrates quantum-inspired optimization algorithms with deep learning architectures to analyze the complex, non-linear relationships between global commodity price volatility and exchange rate fluctuations. Traditional econometric approaches have struggled to capture the multi-scale, high-dimensional dependencies in these financial time series, particularly during periods of market stress and regime changes. Our methodology employs a hybrid quantum-classical neural network that leverages quantum amplitude estimation for enhanced feature extraction from volatile market data, combined with a multi-headed attention mechanism that identifies temporal dependencies across different time horizons. We develop a unique cross-asset contagion metric that quantifies the propagation of volatility shocks between commodity markets and currency pairs, revealing previously undocumented transmission channels. The model was trained on fifteen years of high-frequency data across twenty-three major commodities and thirty-eight currency pairs, demonstrating superior predictive accuracy compared to conventional vector autoregression and recurrent neural network approaches. Our findings reveal asymmetric response patterns where energy commodities exhibit stronger influence on emerging market currencies, while precious metals demonstrate bidirectional causality with safe-haven currencies during crisis periods. The research contributes a new computational paradigm for financial market analysis that bridges quantum computing concepts with practical economic forecasting challenges, offering insights for risk management and monetary policy formulation in increasingly interconnected global markets.