Forecasting volatility returns of oil price using gene expression programming approach.

DOI10.1515/jtse-2017-0022OpenAlexW2907626188WikidataQ128686474 ScholiaQ128686474MaRDI QIDQ2417034

Jingchun Feng, Alexander Amo Baffour, Liwei Fan, Beryl Adormaa Buanya

Publication date: 11 June 2019

Published in: Journal of Time Series Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/jtse-2017-0022

zbMATH Keywords

GARCH modelsgene expression programmingoil price volatilityoil spot and future prices

Mathematics Subject Classification ID

Applications of statistics to economics (62P20)

Uses Software

• MCS

Cites Work

• The Model Confidence Set
• Modeling and forecasting exchange rate volatility in time-frequency domain
• Comparison of value-at-risk models using the MCS approach
• An application of genetic algorithm in a marketing oriented inventory model with interval valued inventory costs and three-component demand rate dependent on displayed stock level
• A hybrid system with filter approach and multiple population genetic algorithm for feature selection in credit scoring
• Generalized autoregressive conditional heteroscedasticity
• What is an oil shock?
• Forecasting using predictive likelihood model averaging
• An application of genetic algorithm in solving an inventory model with advance payment and interval valued inventory costs
• Gene expression programming. Mathematical modeling by an artificial intelligence.
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
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