非线性挥发性时间序列的波动动态和信息流的分析:来自加密货币数据的证据
Muhammad Sheraz1,2, Silvia Dedu3, Vasile Preda4,5,6
1Department of Mathematical Sciences, Institute of Business Administration, The School of Mathematics and Computer Science, Karachi 75270, Pakistan.
这项研究揭示了加密货币波动性之间的长期记忆和显著的信息流. 开放-高-低-关闭估计器有效量化这些复杂的动态,为市场行为提供新的见解.
科学领域:
- 量化金融 量化金融
- 计算经济学的计算经济学
- 金融计量经济学 金融计量经济学
背景情况:
- 加密货币市场表现出高波动性和复杂的动态.
- 了解信息流和长期记忆对于金融建模至关重要.
- 现有的波动性估计器可能无法完全捕捉加密货币特定的行为.
研究的目的:
- 实证地检查五种加密货币估计波动率之间的长内存和双向信息流.
- 评估各种开放-高-低-关闭 (OHLC) 波动性估计器的有效性.
- 通过先进的测量量来量化信息流.
主要方法:
- 雇佣了Garman和Klass (GK),帕金森,罗杰斯和萨切尔 (RS) 和GK-YZ的波动性估计师.
- 使用转移 (TE),有效转移 (ETE) 和雷尼转移 (RTE) 来量化信息流.
- 应用Hurst指数计算来评估日志回报率和波动性中的长内存.
主要成果:
- 证实了加密货币日志回报率和波动性的长期依赖性和非线性行为.
- 在所有OHLC波动性估计中,TE和ETE估计在统计学上是显著的.
- 确定了从BTC到LTC波动 (RS) 以及BNB和XRP波动 (GK,帕金森,GK-YZ) 之间的重要信息流.
结论:
- OHLC波动估计器对于量化加密货币的信息流是非常实用的.
- 该研究为传统的波动性估计方法提供了有价值的替代方案.
- 这些发现有助于我们更好地理解在波动的加密货币市场中的相互联系和记忆.
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