A Generalized Autoregressive Conditional Heteroscedasticity GARCH for Forecasting and Modeling Crude Oil Price Volatility
DOI:
https://doi.org/10.33423/jabe.v26i6.7385Keywords:
business, economics, crude oil price volatility, GARCH modeling, time series forecasting, autoregressive models, predictive analytics, energy and market dynamicsAbstract
This current study explores the application of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to forecast and model crude oil price volatility. Crude oil is a vital commodity whose price fluctuations significantly impact global economies, energy markets, and strategic decisions of both National Oil Companies (NOCs) and International Oil Corporations (IOCs). Using the GARCH(1,1) and GARCH(1,2) models, this study evaluates the effectiveness of these models in capturing the dynamic nature of oil price volatility. The findings indicate that while both models fit the data well, the GARCH(1,1) model is preferred due to its parsimonious nature and comparable forecast accuracy. Despite including an additional lag in the GARCH(1,2) model, it did not significantly outperform the GARCH(1,1) model in predictive performance. The study further analyzes the residuals and autocorrelation characteristics, highlighting the potential for model refinement. The study underscores the importance of selecting an appropriate model complexity, incorporating external factors, and exploring advanced methodologies to enhance forecast accuracy. These insights are critical for developing effective risk management strategies and informing policy decisions in volatile crude oil markets.
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