Forecasting Volatility Returns of Oil Price Using Gene Expression Programming Approach

Amo Baffour Alexander (), Feng Jingchun, Fan Liwei and Buanya Beryl Adormaa
Additional contact information
Amo Baffour Alexander: School of Business, Hohai University, 8 West Fochenglu, Nanjing211100, China
Feng Jingchun: School of Business, Hohai University, 8 West Fochenglu, Nanjing211100, China
Fan Liwei: School of Business, Hohai University, 8 West Fochenglu, Nanjing211100, China
Buanya Beryl Adormaa: College of Environment, Hohai University, Nanjing211100, China

Journal of Time Series Econometrics, 2019, vol. 11, issue 2, 16

Abstract: This study employs four (4) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) variants namely GARCH (1, 1), Glosten–Jagannathan–Runkle (GJR), Auto Regressive Integrated Moving Average (ARIMA)-GARCH and ARIMA-GJR as benchmark models to assess the performance of a proposed novel Gene Expression Programming (GEP) based univariate time series modeling approach used to conduct ex ante oil price volatility forecasts. The report illustrates that the GEP model is more superior to any of the traditional models on issues relating to both loss functions applied. The GEP model is of a greater volatility forecasting precision at different forecast horizons, therefore. There is also the existence of evidence that GJR and ARIMA-GJR differ in their loss functions, the performance is nevertheless better than GARCH (1, 1) and ARIMA-GARCH. This study conducted herein achieves importance in literature by broadening the application of gene algorithms in finance and forecasting. It also solves the problem of high error associated with the use of GARCH related models in oil price volatility forecasting.

Keywords: oil price volatility; gene expression programming; GARCH models; oil spot and future prices (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jtse-2017-0022 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

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:bpj:jtsmet:v:11:y:2019:i:2:p:16:n:2

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jtse/html

DOI: 10.1515/jtse-2017-0022

Access Statistics for this article

Journal of Time Series Econometrics is currently edited by Javier Hidalgo

More articles in Journal of Time Series Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().