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

Author

Listed:
  • Haika Andrew Mbwambo

    (Assistant Lecturer, College of Business Education, Mwanza, Tanzania)

  • Laban Gaspe Letema

    (Assistant Lecturer, College of Business Education, Mwanza, Tanzania)

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

Suggested Citation

  • Haika Andrew Mbwambo & Laban Gaspe Letema, 2023. "Forecasting volatility in oil returns using asymmetric GARCH models: evidence from Tanzania," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 12(1), pages 204-211, January.
    • Handle: RePEc:rbs:ijbrss:v:12:y:2023:i:1:p:204-211
    DOI: 10.20525/ijrbs.v12i1.2308
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    References listed on IDEAS

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    1. Amélie Charles & Olivier Darné, 2021. "Econometric history of the growth–volatility relationship in the USA: 1919–2017," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 15(2), pages 419-442, May.
    2. Ekong, Christopher N. & Onye, Kenneth U., 2017. "Application of Garch Models to Estimate and Predict Financial Volatility of Daily Stock Returns in Nigeria," MPRA Paper 88309, University Library of Munich, Germany.
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