Risk Connectedness Heterogeneity in the Cryptocurrency Markets

Abstract

This paper examines the risk connectedness across seven cryptocurrencies, Bitcoin, Ethereum, Ripple, Litecoin, Stellar, Monero, and Dash, which have large capitalizations in the cryptocurrency market. The data sample is from August 7, 2015, to February 15, 2020. We measure the return risks of the cryptocurrencies by using the CAViaR model, showing that they have similar risk tendencies, with volatility clusterings from the beginning of 2017 to the end of 2018. The net pairwise spillover index developed by Diebold and Yilmaz [1] is used as the measure of the risk connectedness among the cryptocurrencies. We find that the risk spillover directions are highly correlative with the market capitalizations of the cryptocurrencies. Cryptocurrencies with small market capitalization transmit risks to those with large market capitalization. When there is a downward risk tendency, the risk spillover levels among the cryptocurrencies are stronger than when there is an upward risk tendency, while the spillover directions remain the same under both risk tendencies, except for the cryptocurrency Monero, the particularity of which may be due to the difference in its trading volume compared to the others. We use generalized forecast error variance decomposition for the spillover index and explore the risk connectedness across the cryptocurrencies at different timescales, namely, the short term (0-4 days), medium term (4-30 days) and long term (30-300 days). The risk spillovers can be neglected at the short-term frequency, which implies a delayed effect. The risk spillovers at medium-term frequency are mostly stronger than those at long-term frequency. The dynamic connectedness results show that the means of risk spillover at a long-term frequency are larger than those at medium-term frequency. An inverse result holds for the ranges of risk spillover. The fluctuations of risk spillover at long-term and medium-term frequencies admit the same comparison result with the means of risk spillover in these two frequencies. The findings in this paper provide some suggestions for regulators controlling market stability and cryptocurrency investors generating investment strategies.

Does the Land Market Have an Impact on Green Total Factor Productivity? A Case Study on China

“…With the financial support of local government, infrastructures, such as traffic roads, have been further improved, reducing the cost of commodity transportation in various industries. At the same time, foreign advanced technologies will be introduced, which will intensify the technology and commodity exchange among regions, and coordinate development and progress among different industries, thus rationalizing the allocation of social resources and improving the utilization rate of elements; thus, the green total factor productivity will be promoted [62].…”

“…In particular, some areas that rely on land transfer mode need to change their development mode to diversified development and sustainable development. Secondly, while increasing the investment in innovation, the local government should also improve the supervision on the effectiveness of enterprise innovation [62], avoid the problems of inefficient and ineffective innovation, and promote the sustainable development of the region through green technology innovation [63]. At the same time, the local government should strengthen infrastructure construction, improve traffic road facilities, reduce the transportation cost of enterprises, attract a lot of excellent foreign investment, and introduce more advanced equipment and technology.…”

Research on the Impact of Green Technology Innovation on Energy Total Factor Productivity, Based on Provincial Data of China

“…From the discrete degree of these indicators, it can be seen that there is a large standard deviation in the three variables of the economic development level, energy price, and green technology innovation level, which indicates that heterogeneity exists in the field of green technology innovation and energy technology. Heterogeneity research can be analyzed from different perspectives and methods to explore the development law of things and the internal relations of some influencing factors in a more comprehensive way (Li et al, 2020a;Li et al, 2020b;Li and Zhong, 2020;Li et al, 2021a;Li et al, 2021b).…”

Is there a moderate range of impact of financialization on corporate R&D?

“…From the above empirical results, we can understand the impact of financialization on corporate R&D on the whole, and obtain the moderate range of financialization as well. However, heterogeneous effects are widely investigated in the study of financial issues [41][42][43], the effects of financialization are different among enterprises with different R&D intensity. Therefore, this paper further studies the heterogeneity of relationship between financialization and corporate R&D among enterprises with different R&D intensity.…”