摘要翻译：
传统的金融模型无法解释加密货币的经济和货币属性，因为后者具有双重性质：一方面是作为金融资产的使用，另一方面是与底层区块链结构的紧密联系。为了通过统一的方法检查这两个组成部分，我们应用了最近开发的非齐次隐马尔可夫(NHHM)模型，该模型在比特币(BTC)和以太(ETH)价格数据上扩展了一组金融和区块链特定协变量。基于可观察序列，NHHM模型为加密货币市场的潜在微观结构提供了一个新的视角，并提供了对投资者、交易员和矿工行为等不可观察参数的洞察力。该算法识别了比特币生态系统中固有不同活动的两个交替周期（隐藏状态）--基本交易者与不知情或噪声交易者--并揭示了两种状态在短期/长期动态和金融特征方面的差异，如重要的解释变量、极端事件和变化的序列自相关性。一个有点出乎意料的结果是，比特币和以太市场被发现受到明显不同的指标的影响，尽管它们被认为是相关的。目前的方法支持了早期的发现，即加密货币不同于任何传统金融资产，并朝着通过更全面的视角理解加密货币市场迈出了第一步。
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英文标题：
《A Peek into the Unobservable: Hidden States and Bayesian Inference for
  the Bitcoin and Ether Price Series》
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作者：
Constandina Koki, Stefanos Leonardos and Georgios Piliouras
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最新提交年份：
2021
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
  Conventional financial models fail to explain the economic and monetary properties of cryptocurrencies due to the latter's dual nature: their usage as financial assets on the one side and their tight connection to the underlying blockchain structure on the other. In an effort to examine both components via a unified approach, we apply a recently developed Non-Homogeneous Hidden Markov (NHHM) model with an extended set of financial and blockchain specific covariates on the Bitcoin (BTC) and Ether (ETH) price data. Based on the observable series, the NHHM model offers a novel perspective on the underlying microstructure of the cryptocurrency market and provides insight on unobservable parameters such as the behavior of investors, traders and miners. The algorithm identifies two alternating periods (hidden states) of inherently different activity -- fundamental versus uninformed or noise traders -- in the Bitcoin ecosystem and unveils differences in both the short/long run dynamics and in the financial characteristics of the two states, such as significant explanatory variables, extreme events and varying series autocorrelation. In a somewhat unexpected result, the Bitcoin and Ether markets are found to be influenced by markedly distinct indicators despite their perceived correlation. The current approach backs earlier findings that cryptocurrencies are unlike any conventional financial asset and makes a first step towards understanding cryptocurrency markets via a more comprehensive lens.
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PDF链接：
https://arxiv.org/pdf/1909.10957