Enhancing Volatility Forecasting: A Comparative Study of GARCH(1,1) Models with Non-Normal Error Distributions
1 Department of Mathematics, Obafemi Awolowo University, Ile Ife 220005, Nigeria
* Corresponding author: olusan02@yahoo.ca
* Corresponding author: olusan02@yahoo.ca
Abstract
This study proposes a GARCH(1, 1) model with generalized logistic distribution (GLD) errors to better capture
skewness and kurtosis in financial returns. Using crude
oil price data, it compares GLD with GED and Student’s
t-distributions. Results show the GLD-based model outperforms others in forecasting accuracy and volatility modeling.
Keywords
GARCH process
Generalized error distribution
Generalized logistic distribution
volatility
How to Cite
Makinde, O. S., Olosunde, A. A., & Agunloye, O. K. (2025). Enhancing Volatility Forecasting: A Comparative Study of GARCH(1,1) Models with Non-Normal Error Distributions. Nigerian Journal of Mathematics and Applications, 35(1), 43-50. https://doi.org/10.67897/njma.2025.l5iurwxm
O. S. Makinde, A. A. Olosunde, and O. K. Agunloye, "Enhancing Volatility Forecasting: A Comparative Study of GARCH(1,1) Models with Non-Normal Error Distributions," Nigerian Journal of Mathematics and Applications, vol. 35, no. 1, pp. 43-50, June 2025. doi: 10.67897/njma.2025.l5iurwxm
