波动性变化的:评估波动性时间序列规律性的新方法
1Faculty of Computer Science, Bialystok University of Technology, Wiejska 45a, 15-351 Białystok, Poland.
Entropy (Basel, Switzerland)
|March 28, 2025
概括
这项研究揭示了股票市场波动时间序列的高顺序规律性,表明可预测性. 这些发现适用于不同的波动估计器和市场状况,包括COVID-19大流行.
科学领域:
- 量化金融 量化金融
- 金融计量经济学 金融计量经济学
- 时间序列分析时间序列分析
背景情况:
- 波动性时间序列分析对于金融风险管理和市场预测至关重要.
- 评估财务数据中的顺序模式的现有方法存在局限性.
- 了解波动动态是制定强大的交易策略的关键.
研究的目的:
- 引入和评估一种新的方法来评估波动时间序列中的顺序规律性.
- 分析股票市场波动的复杂性和顺序模式,使用修改的香农.
- 为了调查COVID-19大流行对波动时间序列规律性的影响.
主要方法:
- 使用了三种基于每日范围的波动性估计器:帕金森,加曼-克拉斯和罗杰斯-萨切尔.
- 采用了一种新的两值编码程序,将波动变化分类为符号序列.
- 应用修改的Shannon和符号序列直方图来测量时间序列的复杂性和规律性.
主要成果:
- 经验结果表明,在所有分析的股票市场指数中,波动时间序列的顺序规律性始终保持在高水平.
- 观察到的规律性无论选择的波动性估计器和分析的时间段 (COVID-19前与大流行前) 是否存在,都会持续存在.
- 正式的统计分析证实了顺序模式的同质性和意义.
结论:
- 该研究证实了股票市场波动的显著顺序规律性,挑战了完全随机性的概念.
- 这些发现暗示,波动时间序列是可预测的,为改进的预测模型提供了机会.
- 这项研究为学术研究人员和金融从业人员在风险管理和算法交易方面提供了宝贵的见解.
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