Analyzing Commodity Market Volatility and Price Forecasting: A GARCH and ARIMA Model Approach

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dc.contributor.authorBhatia, Amit
dc.date.accessioned2025-10-07T08:32:35Z
dc.date.available2025-10-07T08:32:35Z
dc.date.issued2025
dc.descriptionISME
dc.description.abstractCommodity 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.
dc.identifier.issn2323-5233
dc.identifier.urihttps://atlasuniversitylibraryir.in/handle/123456789/1192
dc.language.isoen
dc.publisherEuropean Economic Letters
dc.subjectAlternativeInvestments
dc.subjectGold
dc.subjectSilver
dc.subjectWheat
dc.subjectCrude Oil
dc.subjectPrice Volatility
dc.subjectCommodityTrading
dc.subjectPrice Forecasting
dc.subjectCommodity Markets
dc.subjectForecasting Models
dc.subjectARIMA
dc.subjectGARCH
dc.titleAnalyzing Commodity Market Volatility and Price Forecasting: A GARCH and ARIMA Model Approach
dc.typeArticle

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