标题： 通过马尔可夫链重构加密货币流程 显示英文标题

标题： Reconstructing cryptocurrency processes via Markov chains

摘要： 对加密货币日益增长的关注导致了对数字股票市场的研究不断增加。 通常用于表征标准股票的方法已被应用于数字股票。 其中一种工具是识别市场波动的过程。 作为有趣的随机过程，通常的统计方法是其重建的适当工具。 在那里，除了偶然性外，每当发现确定性模式时，应存在行为成分的描述。 马尔可夫方法在这一努力中处于领先地位。 在本文中， 考虑了一阶到八阶的马尔可夫链，作为一种预测三种主要加密货币动态的方法。 这是通过使用日内收益的经验基础来完成的。 除了预测外，我们还研究了这些随机过程中是否存在潜在的长记忆成分。 结果表明，使用经验概率获得的预测优于随机选择。 显示英文摘要

摘要： The growing attention on cryptocurrencies has led to increasing research on digital stock markets. Approaches and tools usually applied to characterize standard stocks have been applied to the digital ones. Among these tools is the identification of processes of market fluctuations. Being interesting stochastic processes, the usual statistical methods are appropriate tools for their reconstruction. There, besides chance, the description of a behavioural component shall be present whenever a deterministic pattern is ever found. Markov approaches are at the leading edge of this endeavour. In this paper, Markov chains of orders one to eight are considered as a way to forecast the dynamics of three major cryptocurrencies. It is accomplished using an empirical basis of intra-day returns. Besides forecasting, we investigate the existence of eventual long-memory components in each of those stochastic processes. Results show that predictions obtained from using the empirical probabilities are better than random choices.