Systems Dynamics Group, University of São Paulo, 13635-900 Pirassununga, SP, Brazil.
Institute of Engineering, Polytechnic of Porto, Rua Dr. António B. de Almeida 431, 4249-015 Porto, Portugal.
J Adv Res. 2021 Jan 7;32:85-98. doi: 10.1016/j.jare.2020.12.012. eCollection 2021 Sep.
Cryptocurrencies have been attracting the attention from media, investors, regulators and academia during the last years. In spite of some scepticism in the financial area, cryptocurrencies are a relevant subject of academic research.
In this paper, several tools are adopted as an instrument that can help market agents and investors to more clearly assess the cryptocurrencies price dynamics and, thus, guide investment decisions more assertively while mitigating risks.
We consider three methods, namely the Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) and Detrended Fluctuation Analysis, and three indices given by the Hurst and Lyapunov exponents or the Fractal Dimension. This information allows assessing the behaviour of the time series, such as their persistence, randomness, predictability and chaoticity.
The results suggest that, except for the Bitcoin, the other cryptocurrencies exhibit the characteristic of mean reverting, showing a lower predictability when compared to the Bitcoin. The results for the Bitcoin also indicate a persistent behavior that is related to the long memory effect.
The ARFIMA reveals better predictive performance than the ARIMA for all cryptocurrencies. Indeed, the obtained residual values for the ARFIMA are smaller for the auto and partial auto correlations functions, as well as for confidence intervals.
近年来，加密货币一直受到媒体、投资者、监管机构和学术界的关注。尽管金融领域存在一些质疑，但加密货币仍是学术研究的一个重要课题。
本文采用了几种工具作为手段，可以帮助市场参与者和投资者更清楚地评估加密货币价格动态，从而更自信地指导投资决策，同时降低风险。
我们考虑了三种方法，即自回归综合移动平均（ARIMA）、自回归分数阶移动平均（ARFIMA）和去趋势波动分析，以及由赫斯特和李雅普诺夫指数或分形维数给出的三种指数。这些信息可以评估时间序列的行为，例如它们的持久性、随机性、可预测性和混沌性。
结果表明，除了比特币，其他加密货币都表现出均值回复的特征，与比特币相比，它们的可预测性较低。比特币的结果也表明存在持续的行为，这与长期记忆效应有关。
ARFIMA 对所有加密货币的预测性能都优于 ARIMA。实际上，对于自相关和偏自相关函数以及置信区间，ARFIMA 的残差值更小。
