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.

中文翻译：

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使用基因表达编程方法预测油价的波动性回报。

本研究采用了四（4）个广义自回归条件异方差（GARCH）变量，即GARCH（1、1），Glosten-Jagannathan-Runkle（GJR），自回归综合移动平均值（ARIMA）-GARCH和ARIMA-GJR作为基准模型评估提出的新颖的基于基因表达编程（GEP）的单变量时间序列建模方法的性能，该方法用于进行事前油价波动预测。该报告表明，GEP模型在涉及所应用的两种损失函数的问题上均优于任何传统模型。因此，GEP模型在不同的预测范围内具有较高的波动率预测精度。也有证据表明，GJR和ARIMA-GJR的损失函数不同，但其性能仍优于GARCH（1、1）和ARIMA-GARCH。通过扩大基因算法在金融和预测中的应用，本文进行的这项研究在文献中具有重要意义。它还解决了与GARCH相关模型在油价波动预测中使用相关的高误差问题。