探索主要加密货币日志回报之间的相关性:一个隐马尔可夫模型

Pub Date : 2021-11-10 DOI:10.1111/ecno.12193
Fulvia Pennoni, Francesco Bartolucci, Gianfranco Forte, Ferdinando Ametrano
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引用次数: 3

摘要

提出了一个隐马尔可夫模型,用于分析比特币、以太坊、Ripple、莱特币和比特币现金最近4年的每日日志回报时间序列。假设这些对数回归在具有有限数量的状态或状态的潜在马尔可夫过程上有条件地具有多变量高斯分布。隐藏制度代表了通过预期值的不同向量和对数收益的方差-协方差矩阵识别的不同市场阶段,因此它们在波动性方面也有所不同。模型参数的最大似然估计通过期望-最大化算法进行,并且根据最大后验规则对每个时间场合的状态进行奇异预测。结果显示了市场的三个积极和三个消极阶段。在最近的一段时间里,还预测了积极制度的增长趋势。估计了一个相当异质的相关性结构,并检测到比特币与其他加密货币相关性的结构性中期趋势的证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Exploring the dependencies among main cryptocurrency log-returns: A hidden Markov model

A hidden Markov model is proposed for the analysis of time-series of daily log-returns of the last 4 years of Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash. These log-returns are assumed to have a multivariate Gaussian distribution conditionally on a latent Markov process having a finite number of regimes or states. The hidden regimes represent different market phases identified through distinct vectors of expected values and variance–covariance matrices of the log-returns, so that they also differ in terms of volatility. Maximum-likelihood estimation of the model parameters is carried out by the expectation–maximisation algorithm, and regimes are singularly predicted for every time occasion according to the maximum-a-posteriori rule. Results show three positive and three negative phases of the market. In the most recent period, an increasing tendency towards positive regimes is also predicted. A rather heterogeneous correlation structure is estimated, and evidence of structural medium term trend in the correlation of Bitcoin with the other cryptocurrencies is detected.

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