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 48: 561–581; 2020 © 2020 Statistical Society of Canada

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关于本地区块链网络功能在加密货币价格形成中的作用

加密货币和基础的区块链技术最近获得了前所未有的公众关注。与法定货币相反，比特币和莱特币等加密货币交易被永久记录在分布式账本中，以供公众查看。结果，所有加密货币交易的公开可用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，而且可以用来研究各种区块链网络功能与加密货币风险投资之间的关系。我们介绍了小链或区块链主题的新颖概念，以利用此信息。区块链使我们能够评估区块链的局部拓扑结构在比特币和莱特币联合价格形成和动态中的作用。我们调查了小链的预测Granger因果关系，并确定了对加密货币价格和投资风险表现出最高预测影响的某些类型的小链。更广泛地讲，虽然实际上尚未探索区块链数据分析的统计方面，但本文旨在突出区块链数据分析的各种新兴理论，方法论和应用研究挑战，这将引起广大统计界的兴趣。《加拿大统计杂志》 48：561–581；2020©2020加拿大统计学会