预测原油的区间值回报:基于核的新方法

IF 3.4 3区 经济学 Q1 ECONOMICS
Kun Yang, Xueqing Xu, Yunjie Wei, Shouyang Wang
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引用次数: 0

摘要

本文提出了一种新颖的基于核的广义随机区间多层感知器(KG-iMLP)方法,用于预测原油的高波动区间值回报。KG-iMLP 模型是利用基于核函数的距离构建的,其性能优于传统的欧氏距离。此外,利用预测误差的方差-协方差矩阵估算出最优核函数,有助于更好地理解区间值数据的整体特征。核函数的引入使得用于估计机器学习参数的算法失效。因此,本文进一步提出了一种累加误差传播的后向距离算法来估计核函数和模型参数,为在区间神经网络中利用核函数提供了一种可行的方法。在对 WTI 原油周收益率和日收益率的实证分析中,证明了所提出的方法具有卓越的预测性能,能够对点值和区间值进行稳定而准确的预测。该模型在不同的网络结构中表现出一致的出色性能,展示了 KG-iMLP 在原油价格预测方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting interval-valued returns of crude oil: A novel kernel-based approach

This paper proposes a novel kernel-based generalized random interval multilayer perceptron (KG-iMLP) method for predicting high-volatility interval-valued returns of crude oil. The KG-iMLP model is constructed by utilizing the D K distance based on a kernel function, which outperforms the conventional Euclidean distance. Additionally, the optimal kernel function is estimated using the variance–covariance matrix of the prediction error, contributing to a better understanding of the overall characteristics of interval-valued data. The introduction of the kernel function renders the algorithms used for estimating machine learning parameters ineffective. Therefore, this paper further proposes a backward D K distance of accumulative error propagation algorithm to estimate both the kernel function and model parameters, which provides a feasible approach for utilizing kernel function in interval neural networks. In the empirical analysis of weekly and daily returns of WTI crude oil, the superior predictive performance of the proposed method is demonstrated, enabling stable and accurate predictions for both point values and interval values. The model exhibits consistent outstanding performance across different network structures, showcasing the potential of KG-iMLP for crude oil price forecasting.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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