Forecasting volatility in oil returns using asymmetric GARCH models: evidence from Tanzania

Haika Andrew Mbwambo and Laban Gaspe Letema
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Haika Andrew Mbwambo: Assistant Lecturer, College of Business Education, Mwanza, Tanzania
Laban Gaspe Letema: Assistant Lecturer, College of Business Education, Mwanza, Tanzania

International Journal of Research in Business and Social Science (2147-4478), 2023, vol. 12, issue 1, 204-211

Abstract: Crude oil is, without a doubt, one of the most significant commodities in the modern world. The highly contagious coronavirus, the conflict between Ukraine and Russia, and not to mention the unusual turn of events worldwide have all significantly impacted crude oil prices. Since oil is required for all critical economic activities, such as production and transportation, a forecast for crude oil prices is essential. Using a range of GARCH models at such an intense time, this study attempted to close this gap by forecasting crude oil volatility. To forecast the returns of Brent crude oil prices from January 2002 to February 2022, this study uses a family of GARCH models. In the respective family of models, GJRGARCH (1,1) was the most effective in predicting the volatility of crude oil prices. The GJRGARCH model was chosen since it had a higher likelihood value and a lower information criteria value. A diagnostic check was done to evaluate the produced model further to ensure that the proposed model was good enough for forecasting crude oil volatility. The study suggests employing the GJRGARCH technique to predict future fluctuations in exceptional circumstances. Key Words:Brent, crude oil, GJRGARCH, forecasting, returns, volatility

Date: 2023
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