{"id":24496,"date":"2026-01-17T15:45:23","date_gmt":"2026-01-17T15:45:23","guid":{"rendered":"https:\/\/scientificassociation.org\/?post_type=journal-paper&#038;p=24496"},"modified":"2026-01-17T15:45:23","modified_gmt":"2026-01-17T15:45:23","slug":"modeling-crude-oil-price-volatility-in-nigeria-using-garch-11-egarch-11-and-gjr-garch-11-models","status":"publish","type":"journal-paper","link":"https:\/\/scientificassociation.org\/ar\/journal-paper\/modeling-crude-oil-price-volatility-in-nigeria-using-garch-11-egarch-11-and-gjr-garch-11-models\/","title":{"rendered":"Modeling crude oil price volatility in Nigeria: using GARCH (1,1), EGARCH (1,1), and GJR-GARCH (1,1) models"},"content":{"rendered":"<div class=\"padding_abstract justify ltr\">This study investigates the performance of various GARCH models for volatility forecasting, focusing on the GARCH (1,1), EGARCH (1,1), and GJR-GARCH (1,1) frameworks, each tested with normal and Student\u2019s t-distributions. The models are evaluated using four information criteria: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Shibata Criterion, and Hannan-Quinn Criterion. The results reveal that the GARCH (1,1)-NORM model consistently outperforms other models in terms of all criteria, demonstrating the lowest values across AIC, BIC, Shibata, and Hannan-Quinn measures. In contrast, models using the student\u2019s t-distribution (-STD) generally exhibit slightly higher information criteria values compared to their normal distribution counterparts (NORM). This suggests that the normal distribution provides a better fit for the data in this analysis. Additionally, while more complex models such as EGARCH and GJR-GARCH offer advanced features like capturing asymmetry and leverage effects, they do not significantly improve model performance over the simpler GARCH (1,1)-NORM model. The study recommends using the GARCH (1,1)-NORM model for its optimal balance of fit and simplicity while acknowledging that alternative models like ARIMA-GARCH, TARCH, and Stochastic Volatility models might be explored based on specific data characteristics and forecasting needs.<\/div>\n","protected":false},"featured_media":24500,"template":"","meta":{"_acf_changed":false},"journal-name":[219],"paper-tag":[235],"class_list":["post-24496","journal-paper","type-journal-paper","status-publish","has-post-thumbnail","hentry","journal-name-jcese","paper-tag--5--1"],"acf":[],"_links":{"self":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-paper\/24496","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-paper"}],"about":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/types\/journal-paper"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/media\/24500"}],"wp:attachment":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/media?parent=24496"}],"wp:term":[{"taxonomy":"journal-name","embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-name?post=24496"},{"taxonomy":"paper-tag","embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/paper-tag?post=24496"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}