Purpose

This paper examines the return and volatility spillovers among major cryptocurrency using daily data from 10/08/2015 to 15/04/2018.

Design/methodology/approach

The authors employ the Dielbold and Yilmaz (2012) spillover approach and rolling sample analysis to capture the inherent secular and cyclical movements in the cryptocurrency market.

Findings

The authors show that there is substantial difference between the behaviour of the cryptocurrency portfolios return and volatility spillover indices over time. The authors find evidence of interdependence among cryptocurrency portfolios given the spillover indices. While the return spillover index reveals increased integration among the currency portfolios, the volatility spillover index experiences significant bursts during major market crises. Interestingly, return and volatility spillovers exhibit both trends and bursts respectively.

Originality/value

This study makes a methodological contribution by adopting Dielbold and Yilmaz (2012) approach to quantify the returns and volatility transmissions among cryptocurrencies. To the best of our knowledge, little or no study has adopted the Dielbold and Yilmaz (2012) methodology to investigate this dynamic relationship in the cryptocurrencies market. The Dielbold and Yilmaz (2012) approach provides a simple and intuitive measure of interdependence of asset returns and volatilities by exploiting the generalized vector autoregressive framework, which produces variance decompositions that are unaffected by ordering.

中文翻译：

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加密货币投资组合中的回报率和波动率溢出

目的

本文使用从10/08/2015到15/04/2018的每日数据，研究了主要加密货币之间的回报率和波幅溢出。

设计/方法/方法

作者采用Dielbold和Yilmaz（2012）的溢出方法和滚动样本分析来捕获加密货币市场固有的长期和周期性运动。

发现

作者表明，随着时间的流逝，加密货币投资组合回报的行为与波动性溢出指数之间存在实质性差异。作者发现，在存在溢出指数的情况下，加密货币投资组合之间存在相互依赖性的证据。尽管收益溢出指数显示出各货币投资组合之间的整合程度有所提高，但在主要市场危机期间，波动溢出指数却经历了重大爆发。有趣的是，收益率和波动率溢出分别显示趋势和爆发。

创意/价值

这项研究通过采用Dielbold和Yilmaz（2012）方法来量化加密货币之间的收益和波动率传递，为方法学做出了贡献。据我们所知，很少或没有研究采用Dielbold and Yilmaz（2012）方法来研究加密货币市场中的这种动态关系。Dielbold和Yilmaz（2012）的方法通过利用广义矢量自回归框架提供了简单，直观的资产收益率和波动率相互依存性度量，该框架会产生不受阶数影响的方差分解。