论文标题
主要加密货币市场回报的自相关
Autocorrelation of returns in major cryptocurrency markets
论文作者
论文摘要
本文是探讨主要加密货币市场效率的一系列简短文章中的第一篇。许多统计测试和统计分布的属性将用于评估加密货币市场是否有效，以及它们的效率如何随时间变化。在本文中，我们使用以下方法分析了主要加密货币市场的回报的自相关：Pearson的不同订单的自相关系数，Ljung-Box测试和一阶Pearson在滚动窗口中的自相关系数。所有实验均在BTC/USD，ETH/USD，BITFINEX交换的ETH/BTC市场以及Bitmex Exchange上的XBT/USD市场上进行，每个市场都在5分钟，1小时，1天和1周的时间范围内进行。结果在图表上以视觉表示。在所有市场的5m和1h时间范围内，统计上显着的自相关持续存在。测试在1D和1W时间范围内不同意。本文的结果是完全可重现的。使用的数据集，源代码和可运行的Jupyter笔记本可以在GitHub上找到。
This paper is the first of a series of short articles that explore the efficiency of major cryptocurrency markets. A number of statistical tests and properties of statistical distributions will be used to assess if cryptocurrency markets are efficient, and how their efficiency changes over time. In this paper, we analyze autocorrelation of returns in major cryptocurrency markets using the following methods: Pearson's autocorrelation coefficient of different orders, Ljung-Box test, and first-order Pearson's autocorrelation coefficient in a rolling window. All experiments are conducted on the BTC/USD, ETH/USD, ETH/BTC markets on Bitfinex exchange, and the XBT/USD market on Bitmex exchange, each on 5-minute, 1-hour, 1-day, and 1-week time frames. The results are represented visually on charts. Statistically significant autocorrelation is persistently present on the 5m and 1H time frames on all markets. The tests disagree on the 1D and 1W time frames. The results of this article are fully reproducible. Used datasets, source code, and a runnable Jupyter Notebook are available on GitHub.
