The objective of this work is to understand the dynamics of cryptocurrency prices. Specifically, how prices switch between different regimes, going from “bull” to “stable” and “bear” times. For this purpose, we propose a hidden Markov model that aims at explaining the evolution of Bitcoin prices through different, unobserved states. The implementation of the proposed model includes a likelihood ratio test that allows to compare models with different states and with different covariance structures. Our empirical findings show that the time movements of Bitcoin prices across different exchange markets are well‐described by the proposed model. In particular, a parsimonious model with a diagonal covariance matrix leads to better predictions, compared with a model with a full covariance matrix.

中文翻译：

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隐藏的马尔可夫模型以检测加密资产市场中的政权变化

这项工作的目的是了解加密货币价格的动态。具体来说，价格如何在不同的制度之间转换，从“牛市”到“稳定”和“熊市”时期。为此，我们提出了一个隐马尔可夫模型，旨在解释不同的，未观察到的状态下比特​​币价格的演变。提出的模型的实现包括似然比检验，该检验允许比较具有不同状态和具有不同协方差结构的模型。我们的经验发现表明，所提出的模型很好地描述了不同交易所市场上比特币价格的时间变化。特别地，与具有完整协方差矩阵的模型相比，具有对角协方差矩阵的简约模型可以带来更好的预测。