论文标题
比特币市场的时变波动和微小频率的信息流动
Time-varying volatility in Bitcoin market and information flow at minute-level frequency
论文作者
论文摘要
在本文中，我们通过广泛回归自动回归有条件的异性恋（GARCH）家族的统计模型分析了比特币市场上的分钟价格回报的时间序列。在金融中提出了几种数学模型，以建模价格回报的动态，每个数学模型都对问题引入了不同的观点，但没有任何缺点。我们结合了一种使用回报的历史价值及其挥发性的方法 - Garch模型家族，以及所谓的“分销假设的混合”，该假设指出，价格回报的动态受到市场信息流的影响。使用与比特币相关的推文和交易数量作为外部信息的时间序列，我们测试了在微小的比特币价格时间序列上的几种GARCH模型变体的波动性预测的改善。统计测试表明，最简单的GARCH（1,1）对在样本外数据上添加外部信号的添加对模型波动过程做出了反应。
In this paper, we analyze the time-series of minute price returns on the Bitcoin market through the statistical models of generalized autoregressive conditional heteroskedasticity (GARCH) family. Several mathematical models have been proposed in finance, to model the dynamics of price returns, each of them introducing a different perspective on the problem, but none without shortcomings. We combine an approach that uses historical values of returns and their volatilities - GARCH family of models, with a so-called "Mixture of Distribution Hypothesis", which states that the dynamics of price returns are governed by the information flow about the market. Using time-series of Bitcoin-related tweets and volume of transactions as external information, we test for improvement in volatility prediction of several GARCH model variants on a minute level Bitcoin price time series. Statistical tests show that the simplest GARCH(1,1) reacts the best to the addition of external signal to model volatility process on out-of-sample data.
