Average volume bitcoin
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Therefore, we choose to collect the Bitcoins price data on Sunday as it is the last day in the week. Concomitantly this does not require correction for the insufficient data, for instance like stock markets which only open until Friday. Furthermore, Google Trends are completely extracted from the open-source provided by Google.
In addition, we adjusted some of the insufficient data collected from Google Trends to have a continuous time series. However, in the Weeks with no data were skipped and returns and volume were adjusted to balance the dataset. This approach was popularized by Campbell and Yogo and is used to construct the volume series, which is also tested for stationarity. A number of studies focusing on volume and returns have followed this approach, most remarkably, Cooper , Odean , Cochrane , and Gebka and Wohar To begin, we performed a descriptive statistical analysis to gain insight into the features of the data.
The results are presented in Table 1.
After the brief description of data, we employed unit root tests to check if the data series is stationary, using the augmented Dickey-Fuller ADF and Phillips-Perron tests. The results presented in Table 2 suggest that the dataset is stationary at levels, i.
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Next, we tested for co-integration using the Johansen cointegrated test for these pairs of variables. The results of the co-integration test presented in Table 3 suggest that there is no co-integrating relationship between any two pairs i. This suggests that the relationship between Google search values and Bitcoin returns and trading volume do not persist in the long run.
This is intuitive, considering the volatility and dynamics of the market.
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Hence, this leads us to a VAR estimation. Before proceeding, we selected the lag order based on the Akaike information criteria and chose three as the optimal number of lags6. To determine the direction of causality, we performed a Granger causality test and the results presented in Table 4.
The results of the Granger causality test showed that there is strong evidence of causality for Bitcoin returns only for the SGSV. This was statistically unidirectional causality running from the SGSV only to returns. This means that Bitcoin returns on can be predicted by the Google search value. This is an intuitive finding, as investors looking for Bitcoin information on the Internet may lead to an increase in the price of Bitcoin, producing a cause-and-effect relationship with Bitcoin returns.
Next, to take a broader perspective on the association among the variables being analyzed, we performed an impulse response function IRF analysis; the results are presented in Figs. The response was also statistically significant and the surge in returns persisted for a period before starting to decline.
This implies that a shock on the search value leads to an increase in returns immediately over the following week. Afterwards, it sharply decreases and ends in the second week. On the other hand, stock returns did not lead to a surge in searches. Moreover, this shock triggered a gradual increase in trading volume over two weeks, and thereafter the effects started to diminish. The remaining pairs of analysis did not show any significant responses, indicating lack of association. Accordingly, we can only infer that one can confidently predict a surge in trading volume in response to a surge in the SGSV.
However, the contribution of the SGSV to volume is comparatively trivial. Investors find more information about Bitcoin by searching, but their trading behavior is not explained by the action of searching. This also implies that those who search do not necessarily enter into transactions.
We also employed a copulas approach with an estimated parameter to define how the dependency holds between the variables of interest. The rationale for enriching our estimation with this approach is a manifested in the notion to perform an inclusive empirical analysis, and b that the assumptions for the previous test are quite strict, whereas copulas meet more requirements for testing dependence structures, including left tailed, right tailed, or normal distributions. The nonparametric approach is a good method for estimating the dependence structure for a pair of random variables, whereas the parametric copulas is the best indicator for identifying the position of tail dependence rather than structure Nguyen et al.
Instead of employing correlation or causality with the disadvantage of scalar measures of dependence or linear estimations, we employ Kendal-plots and copulas to determine the dependence relationship by joining the marginal distribution with the joint distribution of the variables being analyzed. Hence, this approach is an appropriate candidate for use as the framework of analysis. Furthermore, the fluctuation of Bitcoin prices is quite high, depicting substantial nonlinearities; using a traditional approach such as correlation or Granger Causality would be prone to producing spurious results for estimation.
For all these reasons we employed copulas and a nonparametric approach. The results are presented in Table 5 :.
Similarly, from the technological as well economic perspective, Goertzel et al. This gradually improving volume comes during a bullish so far. Quantitative Finance and Economics , , 4 3 : From the Availability Zone drop-down menu, select the same Availability Zone of your current volume from step 3. Assuming blended fees based on volume of the top
With the highest log likelihood, we choose the Gumbel copulas family for estimation. The results suggested that the Google search value has a strong relationship with returns but a comparatively weaker one with volume.
In addition, the Gumbel copulas family right tail indicates joint probabilities for increasing values for both groups. Last, Kendall plots were adopted, which is a graphical approach based on rank statistics. The novelty of this approach is that it allows detection of nonlinear dependence between two variables. Kendal plots are an effective methodology for capturing a dependence structure. In their seminal work, Genest and Boies introduced the Kendall-plot K-plot to investigate dependence between random variables.
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Considering this aspect, we chose Kendall-plots to determine the dependence structure of Bitcoin returns and search engines, as well as trading volume. The results are presented in Fig. The Kendall-plots showed that the points are not linearly distributed along the degree line of the graph, confirming that these series of values are dependence structures. Concomitantly, the findings in this section complement those obtained by the traditional tests. Cryptocurrencies, which are based on blockchain technology and are often called Bitcoin, have recently attracted a lot of debate in socio-economic and financial circles.
The behavior of cryptocurrencies and their dynamics, as well as their predictability, are of prime interest to investors and financial institutions, as well as policymakers. Keeping this interest in context, this brief study has analyzed the predictability of Bitcoin volume and returns using data extracted from the Google search engine. We employ a rich set of established empirical approaches, including the VAR framework, a copulas approach, and non-parametric drawings of time series, which are characterized as continuous, and random variables for capturing the dependence structure.
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Our key findings lead us to conclude that Google search values exert significant influence on Bitcoin returns, particularly in the short run. We also found that Google search values have some influence on the trading volumes of cryptocurrencies, although our results fell just short of statistical significance benchmarks.
The results indicate that there was no long-run relationship; however, there was clear short-term dependency. The more frequently investors look for information, the higher the returns and trading volume that follow. This shock influence lasts at least one week before returning to equilibrium.
By using copulas and a nonparametric approach, we confidently confirm the relationship between search values and Bitcoin returns and volume.
Search tools can generate information, which is swiftly incorporated into the market, and can support investment in and predictability of Bitcoin returns and volume. The proposed approach and framework we employed in this study for Bitcoins can be extended to other cryptocurrencies and asset classes, including both financial and non-financial assets.
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There are also some limitations of this study which provides a rationale for further research in this area. For instance, in the future work, the interactions between Google Trends and cryptocurrencies can be seen through the lens of a time-varying framework such as Time-Varying Copulas. For the future research, fellow scholars might be interested in expanding the analysis to other cryptocurrencies such as Ethereum ETH and Litecoin LTC etc.
Hence, in the future research combining one may consider combining these factors. Physica A: Statistical Mechanics and its Applications — News Markets. By Daniel Phillips 2 min read. Bitcoin trading volume is now at its highest since mid Disclaimer The views and opinions expressed by the author are for informational purposes only and do not constitute financial, investment, or other advice.
Read on the Decrypt App for the best experience. Its median "spread," or difference between the price a seller wants and the price a buyer wants, for bitcoin was about 1 cent. That scenario passed Bitwise's test for having real volume. Exchanges may have an incentive to report fake volume. Bad actors may look to attract listings for new initial coin offerings, or ICOs, who want their cryptocurrency on an exchange where more trading goes on, Bitwise said.
The office of New York Attorney General also flagged the issue in a recent report warning that exchanges are vulnerable. Because most cryptocurrency trading platforms don't use the same monitoring tools as stock exchanges, SEC Chairman Jay Clayton has warned that investors may not get a fair assessment of bitcoin's price.