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Volatility Spillovers Among the Cryptocurrency Time Series

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This post examines the volatility spillover effect among the cryptocurrency time series. The main goal is to determine whether one type of cryptocurrency is affected by another type’s price movement. To this end, we develop a novel methodology based on variance decomposition analysis (VDA) and apply it to six major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC), Dashcoin (DSH) and Monero (XMR). We find that bitcoin volatility is among the most critical factors driving other cryptocurrencies’ volatilities. For news comparison, and review about cryptocurrencies’ financial sideand how to use your android device to invest in Bitcoin,visit this link.

The cryptocurrency market grew to $300 billion in 2018, significantly increasing investors and traders willing to invest their money. However, due to the volatility of this market, it can be difficult for investors to make accurate predictions about its future prices.

Literature Review and Hypothesis Development

To address this issue, they introduce an innovative approach based on network theory and apply it to analyze the cryptocurrency time series data from different perspectives:

  • Volatility spillovers among cryptocurrencies
  • The network structure of cryptocurrencies
  • Risks associated with cryptocurrency investments

This section of your report should be organized logically. Begin with an overview of past research on the topic, including any previous research that has been successful or unsuccessful. This section should also include studies that partially succeeded or failed to replicate their original findings. Finally, discuss the wide variety of factors that may affect volatility spillovers among cryptocurrencies, such as time series length and cryptocurrency market capitalization. And how they might impact your ability to conclude your study.

Data and Methodology

They obtained the cryptocurrency data from CoinMarketCap. The main dataset consists of daily returns for each cryptocurrency over the sample period, which includes ten cryptocurrencies: Bitcoin, Ethereum, Ripple, Bitcoin Cash, Litecoin, IOTA, EOS, Dash, and Monero. All the data on CoinMarketCap is recorded in US dollars (USD). They use annualized daily returns as the dependent variable in our regressions since they are interested in measuring their volatility spillovers to other cryptocurrencies.

Empirical Results and Spillover Network Analysis

Data analysis shows that bitcoin volatility is the most significant factor driving market volatility. First, we examine whether commonality exists in the variance decomposition among cryptocurrencies. Second, we use a contagion model to quantify spillover effects among cryptocurrency time series and understand how they are related.

The first step of our analysis is to examine whether there exists commonality in the variance decomposition among cryptocurrencies. Using monthly data, we do this by computing correlation coefficients between each pair of cryptocurrencies’ returns from January 2016 to April 2018. The period range is chosen because it contains no missing values.

The results of the variance decomposition analysis show that bitcoin volatility is the most significant factor in driving the whole cryptocurrency market volatility. This finding is consistent with our earlier study, which shows that bitcoin strongly influences other cryptocurrencies. However, they find that there are spillovers between different cryptocurrencies as well. Their findings suggest that it may be worthwhile to study whether there are correlations between these cryptocurrencies and their rates of return.

The results of the variance decomposition analysis show that bitcoin volatility is the most significant factor in driving the whole cryptocurrency market volatility. The other cryptocurrencies are also affected by bitcoin volatility but to a lesser extent. In addition, they found no significant variation spillover among these cryptocurrency time series.

There are spillovers between cryptocurrency time series. In this paper, we have looked at the volatility of a select few cryptocurrencies and found significant correlations among them. Furthermore, it can explain these correlations in terms of fundamental factors such as network size and market capitalization. It is important to remember that the results presented in this paper are preliminary and should be treated with caution. However, they provide a valuable starting point for further research into the dynamics of cryptocurrency markets.


They found that bitcoin has become a key driver of market volatility in the cryptocurrency space. The results show a high degree of spillover among the cryptocurrencies and within each cryptocurrency. This suggests that as investors get more comfortable with bitcoin trading, they may also start to move into other coins. This would mean a higher risk for those who are not actively diversified across different cryptocurrencies at this point

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