In this paper, we explore some stylized facts of the Bitcoin market using the BTC-USD exchange rate time series of historical intraday data from 2013 to 2020. Despite Bitcoin presenting some very peculiar idiosyncrasies, like the absence of macroeconomic fundamentals or connections with underlying assets or benchmarks, an asymmetry between demand and supply and the presence of inefficiency in the form of strong arbitrage opportunity, all these elements seem to be marginal in the definition of the structural statistical properties of this virtual financial asset, which result to be analogous to general individual stocks or indices. In contrast, we find some clear differences, compared to fiat money exchange rates time series, in the values of the linear autocorrelation and, more surprisingly, in the presence of the leverage effect. We also explore the dynamics of correlations, monitoring the shifts in the evolution of the Bitcoin market. This analysis is able to distinguish between two different regimes: a stochastic process with weaker memory signatures and closer to Gaussianity between the Mt. Gox incident and the late 2015, and a dynamics with relevant correlations and strong deviations from Gaussianity before and after this interval.

在本文中，我们使用2013年至2020年的历史盘中数据的BTC-USD汇率时间序列，探索了比特币市场的一些典型事实。尽管比特币呈现出一些非常特殊的特质，例如缺乏宏观经济基本面或与基础资产的联系或基准，供需之间的不对称以及以强套利机会的形式存在的低效率，所有这些要素在此虚拟金融资产的结构统计属性的定义中似乎都是微不足道的，其结果类似于一般个别股票或指数。相反，与法定货币汇率时间序列相比，我们发现线性自相关值存在更明显的差异，更令人惊讶的是，存在杠杆效应。我们还将探索相关性的动态，监控比特币市场发展的变化。该分析能够区分两种不同的状态：具有较弱记忆特征和更接近高斯山的高斯性的随机过程。此间隔前后的高斯事件和2015年末，以及具有相关性和与高斯性的强烈偏差的动力学。