使用混合ARIMA-SVR-POT模型预测原油期货的风险价值
1Business School, Ningbo University, 315211, Ningbo, China.
Heliyon
|January 1, 2024
概括
通过混合ARIMA-SVR-POT模型,预测原油期货风险价值 (VaR) 得到了改进. 这种先进的方法准确地预测回报,并捕捉极端的市场风险,优于传统方法.
科学领域:
- 量化金融 量化金融
- 风险管理 风险管理
- 计量经济学 计量经济学
背景情况:
- 原油期货呈现高波动性,对准确的风险价值 (VaR) 预测构成挑战.
- 极端价格波动需要强大的模型来捕捉尾部分布风险.
研究的目的:
- 建议和评估混合自回归集成移动平均线-支持向量回归-峰值超过值 (ARIMA-SVR-POT) 模型,用于原油期货 VaR.
- 为了比较ARIMA-SVR-POT与ARIMA-EGARCH,ARIMA-SVR和ARIMA-EGARCH-POT模型的性能.
主要方法:
- 开发一种混合ARIMA-SVR-POT模型,整合时间序列分析,机器学习和极端价值理论.
- 利用WTI原油期货数据从2016年6月23日到2022年9月30日进行实证分析.
- 通过后期测试和Kupiec的测试在各种保证级别的性能评估.
主要成果:
- 在预测原油期货回报率和波动性方面,ARIMA-SVR-POT模型表现出卓越的准确性.
- 混合型号有效地捕获了极端尾部回报,超过了替代型号.
- 后期测试证实了该模型的稳定性,通过了Kupiec测试在多个置信区间 (95%至99.9%).
结论:
- 在波动的原油市场中,ARIMA-SVR-POT模型为VaR计算提供了更精确的潜在损失估计.
- 这种增强的风险管理工具比传统的统计模型提供了更高的性能.
- 这些发现支持采用先进的混合模型来有效管理市场风险.
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