Forecasting Gasoline Market Volatility using Non-Linear Time Series Models

Athanasia Stylianou Kalaitzi () and Evgenia Stylianou Kalaitzi ()
Additional contact information
Athanasia Stylianou Kalaitzi: School of Accounting and Finance, Middlesex University Dubai, United Arab Emirates
Evgenia Stylianou Kalaitzi: Lancaster University Management School, United Kingdom

International Journal of Energy Economics and Policy, 2025, vol. 15, issue 4, 139-151

Abstract: This study forecasts the dynamics of gasoline price returns using daily data from January 2, 1992, to June 6, 2022, and crude oil price returns as a regressor. The non-linear dependence in the volatility of the gasoline return is confirmed and the Markov Switching (MS), the autoregressive conditional heteroskedasticity (ARCH) and the generalized autoregressive conditional heteroskedasticity (GARCH) models are estimated. To account for the linear dependence found in the initial estimates, a GARCH (1,1) model with lagged gasoline returns is used, while a GARCH (1,1) is fitted on the Markov switching residual to capture both the volatility in the conditional mean and variance. The forecasting performance of the estimated models is evaluated, and the GARCH (1,1) on the Markov Switching residual is found to be the best model to forecast the average gasoline returns, while the GARCH (1,1) with linear dependence is preferable for forecasting the volatility of gasoline returns. Identifying the best time series model is crucial for the market participants, and especially for oil companies to evaluate the market situation.

Keywords: Energy Markets; Volatility; Generalized Autoregressive Conditional Heteroskedasticity; Markov Switching (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://econjournals.com/index.php/ijeep/article/download/18825/8948 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eco:journ2:v:15:y:2025:i:4:id:18825

Ordering information: This journal article can be ordered from
https://econjournals.com/index.php/ijeep

DOI: 10.32479/ijeep.18825

Access Statistics for this article

More articles in International Journal of Energy Economics and Policy from International Journal of Energy Economics and Policy
Bibliographic data for series maintained by Monica Sinhat ().