维克斯恒定到期期货交易策略:一个前进的机器学习研究
Sangyuan Wang1, Keran Li1, Yaling Liu1
1Southwestern University of Finance and Economics, Chengdu, Sichuan, China.
PloS one
|April 19, 2024
概括
机器学习模型使用术语结构数据准确预测VIX期货回报. 一个新的受约束平均差异投资组合优化 (C-MVO) 策略显著优于基准,验证了VIX期货期权结构的预测能力.
科学领域:
- 量化金融 量化金融
- 计算金融是指计算金融.
- 机器学习应用 机器学习应用
背景情况:
- 维克斯恒定到期期货 (VIX CMF) 对于对冲和投机至关重要.
- 由于市场波动,预测VIX CMF的回报具有挑战性.
- 术语结构信息提供了潜在的预测见解.
研究的目的:
- 为了评估VIX CMFs期权结构对第二天回报的预测能力.
- 开发和测试基于机器学习预测的新型交易策略.
- 将机器学习模型的性能和拟议的策略与基准进行比较.
主要方法:
- 采用七种先进的机器学习模型进行数值预测.
- 使用了三个特征集,逐步结合VIX CMF的术语结构.
- 在11年的时间里实施了一种前进的扩展窗口方法.
- 提出并测试了受约束平均差异投资组合优化 (C-MVO) 策略.
主要成果:
- 在七个机器学习模型中,有四个实现了预测信息比率>0.02 (平均0.037).
- VIX CMF 的术语结构特征显示出显著的预测能力.
- C-MVO策略的平均信息比率为0.623,超过了基准长短策略 (0.404).
结论:
- 机器学习模型有效地预测VIX CMF的第二天回报.
- VIX CMF 的术语结构包含有价值的预测信息.
- 拟议的C-MVO战略,由机器学习提供动力,提供优越的风险调整回报.
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