Analyzing Commodity Market Volatility and Price Forecasting: A GARCH and ARIMA Model Approach
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Date
2025
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European Economic Letters
Abstract
Commodity trade is a cornerstone of world financial markets, providing investment opportunities, risk management, and price discovery. As commodities are inherently volatile, understanding their price fluctuations and forecasting future trendsis essential. This study examines the performance and volatility of four widely traded commoditiesin the United States -Gold, Silver, Wheat, and Crude Oil using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to measure volatility and the Autoregressive Integrated Moving Average (ARIMA) model to predict future price trends. The GARCH model effectively captures volatility clustering, a key characteristic of financial time series data, while ARIMA analyzes historical patterns for price prediction. Using a decade's worth of dailyhistorical price data from secondary sources, this research provides a robust dataset for in-depth analysis. Additionally, thisstudy highlightsthe need for advanced predictive models that enhance accuracy during market fluctuations. By analyzingGARCH and ARIMA applications in commodity trading, this research contributes to financial modeling and risk management literature, encouraging further exploration of alternative forecasting methods.
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Keywords
AlternativeInvestments, Gold, Silver, Wheat, Crude Oil, Price Volatility, CommodityTrading, Price Forecasting, Commodity Markets, Forecasting Models, ARIMA, GARCH