Risk Connectedness Heterogeneity in the Cryptocurrency Markets - Li, Zhenghui; Wang, Yan; Huang, Zhehao (Frontiers in Physics, 2020)
This paper examines the risk connectedness across the seven cryptocurrencies, Bitcoin, Ethereum, Ripple, Litecoin, Stellar, Monero and Dash, who admit large capitalizations in the cryptocurrency market. The data sample is from August 7, 2015 to February 15, 2020. We apply the CAViaR model to measure the return risks of the cryptocurrencies, showing their similar risk tendencies with volatility clusterings during the beginning of 2017 and the end of 2018. The net pairwise spillover index developed by Diebold and Yilmaz (2012) is use as the measure for the risk connectedness among the cryptocurrencies. We find that the risk spillover directions are highly correlative with the capitalizations of the cryptocurrencies. The cryptocurrencies with small capitalizations transmit risks to those with large cryptocurrencies. In the risk downward tendency, the risk spillover levels among the cryptocurrencies are stronger than that in the risk upward tendency, while the spillover directions keep the same in both risk tendencies, except the cryptocurrency Monero, which may be due to the trading volume difference from the others. We use the generalized forecast error variance decomposition for the spillover index and explore the risk connectedness across the cryptocurrencies in differen time frequencies, including the short term (0-4 days), medium term (4-30 days) and long term (30-300 days) frequency. The risk spillovers in the short term frequency can be neglected, which implies the delay effects of risk spillovers. The risk spillovers in medium term frequency are mostly stronger than that in long term frequency. The dynamic connectedness result shows the risk spillover mean in the long term frequency is larger than that in the medium term frequency. An inverse result holds for the risk spillover range. The risk spillover fluctuations in the long and medium term frequency admit the coincident comparison for spillover levels in these two frequencies. The findings in this paper provide suggestions for regulators controlling the market stability and investors generating investment strategies.