Bartolucci Silvia, Kirilenko Andrei
Department of Finance, Imperial College Business School, London SW7 2AZ, UK.
Department of Finance, Cambridge Judge Business School, Cambridge CB2 1AG, UK.
R Soc Open Sci. 2020 Aug 12;7(8):191863. doi: 10.1098/rsos.191863. eCollection 2020 Aug.
We propose a modelling framework for the optimal selection of crypto assets. We assume that crypto assets can be described according to two features: (technological) and (governance). We simulate optimal selection decisions of investors, being driven by (i) their attitudes towards assets' features, (ii) information about the adoption trends, and (iii) expected future economic benefits of adoption. Under a variety of modelling scenarios-e.g. in terms of composition of the crypto assets landscape and investors' preferences-we are able to predict the features of the assets that will be most likely adopted, which can be mapped to macro-classes of existing crypto assets (stablecoins, crypto tokens, central bank digital currencies and cryptocurrencies).
我们提出了一个用于加密资产最优选择的建模框架。我们假设加密资产可以根据两个特征来描述：（技术方面）和（治理方面）。我们模拟投资者的最优选择决策，这些决策受到以下因素驱动：（i）他们对资产特征的态度，（ii）关于采用趋势的信息，以及（iii）采用的预期未来经济效益。在各种建模场景下——例如就加密资产格局的构成和投资者偏好而言——我们能够预测最有可能被采用的资产特征，这些特征可以映射到现有加密资产的宏观类别（稳定币、加密代币、央行数字货币和加密货币）。
