BJAR British Journal of Advanced Research BJAR 3153-709X Ivory and Finch Publishers 10.68263/BJAR-PCH9DCP8 BJAR_JUL_26_013 Original Research Article Comparative Analysis of Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) Models in Forecasting Stock Market Volatility in Nigeria. Raphael Asu Owan https://orcid.org/0009-0007-8667-176X Department of Statistics, Faculty of physical sciences University of Calabar, Calabar, Nigeria. owanraphael@unical.edu.ng Murphy Y. Dooga, Department of Statistics, Faculty of physical sciences University of Calabar, Calabar, Nigeria. Dorcas N. Asu, Department of Statistics, Faculty of physical sciences University of Calabar, Calabar, Nigeria. Doris N. Ojua, Department of Statistics, Faculty of physical sciences University of Calabar, Calabar, Nigeria. 10 08 2026 1 2

This study investigates the comparative performance of Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models in forecasting stock market volatility in Nigeria. The analysis is based on stock return data obtained from the Nigerian Exchange Group. The study employed time series econometric techniques including the Augmented Dickey-Fuller (ADF) test, ARCH-LM test, and volatility model estimation using ARCH and GARCH frameworks. The empirical results revealed that the stock return series is stationary at level, with an ADF test statistic of approximately −6.73 and a probability value of 0.0000. The ARCH-LM test confirmed the presence of significant ARCH effects, with a probability value of about 0.0006, indicating strong volatility clustering in the Nigerian stock market. The ARCH (1) model estimation showed a statistically significant ARCH coefficient of approximately 0.432 (p < 0.05), confirming that past shocks significantly influence current volatility. However, the model exhibited limitations in capturing persistent volatility behavior. The GARCH (1,1) model produced improved results, with both ARCH (≈ 0.285) and GARCH (≈ 0.641) coefficients being statistically significant (p < 0.05). The sum of these coefficients (≈ 0.926) indicated high volatility persistence in the Nigerian stock market. Additionally, model comparison based on Akaike Information Criterion (AIC ≈ −5.68), Bayesian Information Criterion (BIC ≈ −5.52), and Root Mean Squared Error (RMSE ≈ 0.018) showed that the GARCH model outperformed the ARCH model in forecasting accuracy. The study concludes that although both models are useful for volatility analysis, the GARCH model provides superior forecasting performance and better captures the dynamic nature of stock market volatility in Nigeria.

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