论文标题
通过贝叶斯隐藏的马尔可夫模型探索加密货币的可预测性
Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models
论文作者
论文摘要
在本文中，我们考虑了各种多状态隐藏的马尔可夫模型，以预测和解释在存在状态（制度）动力学的情况下比特币，以太和波纹返回。此外，我们研究了几种财务，经济和加密货币预测因子对加密货币返回系列的影响。我们的结果表明，具有四个州的非均匀隐藏马尔可夫（NHHM）模型在所有三个系列的所有竞争模型中都具有最佳的一步预测性能。预测密度在单个状态随机步行模型上的主导地位取决于国家捕获具有不同回流特征的交替时期。特别是，这四个州NHM模型将比特币系列的公牛，熊和平静的政权区分开，以及以太和波纹系列的利润和风险不同的时期。同样，在隐藏状态下，它可以确定对加密货币返回的线性和非线性影响的预测变量。这些经验发现为投资组合管理和政策实施提供了重要的见解。
In this paper, we consider a variety of multi-state Hidden Markov models for predicting and explaining the Bitcoin, Ether and Ripple returns in the presence of state (regime) dynamics. In addition, we examine the effects of several financial, economic and cryptocurrency specific predictors on the cryptocurrency return series. Our results indicate that the Non-Homogeneous Hidden Markov (NHHM) model with four states has the best one-step-ahead forecasting performance among all competing models for all three series. The dominance of the predictive densities over the single regime random walk model relies on the fact that the states capture alternating periods with distinct return characteristics. In particular, the four state NHHM model distinguishes bull, bear and calm regimes for the Bitcoin series, and periods with different profit and risk magnitudes for the Ether and Ripple series. Also, conditionally on the hidden states, it identifies predictors with different linear and non-linear effects on the cryptocurrency returns. These empirical findings provide important insight for portfolio management and policy implementation.
