On the role of local blockchain network features in cryptocurrency price formation
On the role of local blockchain network features in cryptocurrency price formation
复制标题
DOI：
10.1002/cjs.11547
复制
发表时间：
2020-03-18
影响因子：
0.6
通讯作者：
Kantarcioglu, Murat
中科院分区：
文献类型：
--
作者：
Dey, Asim K.;Akcora, Cuneyt G.;Kantarcioglu, Murat
加密货币和支撑区块链技术最近获得了前所未有的公众关注。与法定货币不同，比特币和莱特币等加密货币的交易永久记录在分布式分类账上，供公众查看。因此，所有加密货币交易的公开可获得性使我们能够创建一个复杂的金融互动网络，不仅可以用来研究区块链图，还可以研究各种区块链网络功能和加密货币风险投资之间的关系。我们引入了一个新的概念，即区块链主题，以利用这些信息。Chainlet让我们能够评估区块链的局部拓扑结构对比特币和Litecoin联合价格形成和动态的作用。我们研究了链条的预测格兰杰因果关系，并确定了某些类型的链条对加密货币价格和投资风险表现出最高的预测影响。更广泛地说，虽然区块链数据分析的统计方面实际上仍未被探索，但本文旨在突出区块链数据分析的各种新兴理论、方法和应用研究挑战，这些挑战将引起广泛的统计界的兴趣。加拿大统计杂志00：000-000；2020(C)2020年加拿大统计学会
Cryptocurrencies and the underpinning blockchain technology have gained unprecedented public attention recently. In contrast to fiat currencies, transactions of cryptocurrencies, such as Bitcoin and Litecoin, are permanently recorded on distributed ledgers to be seen by the public. As a result, public availability of all cryptocurrency transactions allows us to create a complex network of financial interactions that can be used to study not only the blockchain graph, but also the relationship between various blockchain network features and cryptocurrency risk investment. We introduce a novel concept of chainlets, or blockchain motifs, to utilize this information. Chainlets allow us to evaluate the role of local topological structure of the blockchain on the joint Bitcoin and Litecoin price formation and dynamics. We investigate the predictive Granger causality of chainlets and identify certain types of chainlets that exhibit the highest predictive influence on cryptocurrency price and investment risk. More generally, while statistical aspects of blockchain data analytics remain virtually unexplored, the paper aims to highlight various emerging theoretical, methodological and applied research challenges of blockchain data analysis that will be of interest to the broad statistical community. The Canadian Journal of Statistics 00: 000-000; 2020 (c) 2020 Statistical Society of Canada