The Role of Innovation Distributions in Forecasting Brent Crude Oil Volatility Using Univariate GARCH Models

Njoku Godsgift Chilaka; Isaac Didi Essi; Emaka Amos; Barinaadaa John Nwikpe

doi:10.56201/ijasmt.vol.12.no4.2026pg57.70

📄 Download Paper

The Role of Innovation Distributions in Forecasting Brent Crude Oil Volatility Using Univariate GARCH Models

Njoku Godsgift Chilaka, Isaac Didi Essi, Emaka Amos, Barinaadaa John Nwikpe

Abstract

This study investigates the impact of different error distributions on the performance of univariate GARCH-family models in modeling and forecasting the volatility of Brent crude oil returns from January 2014 to May 2025. Descriptive analysis reveals pronounced fluctuations, volatility clustering, and asymmetric behavior in the return series, including major price declines around 2015–2016 and early 2020, followed by a surge in 2021–2022. Stationarity of returns is confirmed using Augmented Dickey-Fuller and Phillips-Perron tests (ADF = −53.52, p = 0.0001), while ARCH-LM tests indicate significant conditional heteroskedasticity (Chi2 = 365.97, p < 0.01), justifying the use of GARCH-type models. Parameter estimates from sGARCH, EGARCH, and TGARCH models highlight strong volatility persistence (β1 ≈ 0.92– 0.98) and significant leverage effects (γ1 ≈ 0.16–0.44). Model comparison across Normal, GED, Student’s t, and Skewed Student’s t distributions shows that heavy-tailed and skewed distributions improve fit, with the TGARCH (1,1) model under the Skewed Student’s t distribution providing the best performance (LogLik = 9836.94, AIC = −6.7076). The findings underscore the importance of selecting appropriate error distributions to accurately capture volatility dynamics, leverage effects, and extreme market movements, providing guidance for risk management and forecasting in oil markets.

Keywords

Brent crude oilvolatility modelingunivariate GARCHerror distributionsStudent’s t distributionskewed Student’s tfinancial time series

References

Alexander, C., & Lazar, E. (2006). Forecasting, structural breaks and GARCH models: Application to financial markets. Journal of International Money and Finance, 25(7), 1138–1155. Ardia, D., Bluteau, K., & Rüede, M. (2019). GARCH models: Structure, statistical inference and financial applications. Wiley. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223–236. Degiannakis, S., & Floros, C. (2022). Do alternative error distributions enhance GARCH volatility forecasts? Journal of Forecasting, 41(4), 597–614. Fernández, C., & Steel, M. F. J. (1998). On Bayesian modeling of fat tails and skewness. Journal of the American Statistical Association, 93(441), 359–371. Ghalanos, A. (2020). rugarch: Univariate GARCH models. R package version 1.4-6. Hansen, B. E. (1994). Autoregressive conditional density estimation. International Economic Review, 35(3), 705–730. Hansen, P. R., Huang, Z., & Shek, H. H. (2012). Realized GARCH: A joint model for returns and realized measures of volatility. Journal of Applied Econometrics, 27(6), 877–906. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. Nugroho, D. B., Priyono, A., & Susanto, B. (2021). Skew normal and skew student-t distributions on GARCH(1,1) model. ResearchGate. https://www.researchgate.net/publication/352839028 Patton, A. J., Ziegel, J. F., & Chen, R. (2019). Dynamic co-VaR. Journal of Financial Econometrics, 17(2), 252–280. Poon, S.-H., & Granger, C. W. J. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41(2), 478–539. Silvennoinen, A., & Teräsvirta, T. (2009). Multivariate GARCH models. Handbook of Financial Time Series. Springer. Tsay, R. S. (2010). Analysis of financial time series (3rd ed.). Wiley.

The Role of Innovation Distributions in Forecasting Brent Crude Oil Volatility Using Univariate GARCH Models

Abstract

Keywords

References

More Articles from INTERNATIONAL JOURNAL OF APPLIED SCIENCES AND MATHEMATICAL THEORY