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Cryptocurrency Bitcoin is the biggest name in cryptocurrency in terms of both price index and market capitalization. Given the growing public investment into crypto, Bitcoin and other established currencies, such as Ethereum and Ripple, are facing increased competition from new, well-funded cryptocurrencies, which raised 52 million U. Blockchain experts envision a huge amount of possible applications, with everything from supply chain management to online personal identification.

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Statista Accounts: Access All Statistics. Basic Account. You only have access to basic statistics. The approached methodology follows the traditional econometric method of VECM in combination with the time-frequency domain analysis via Wavelet techniques. VECM restricts the long run behavior of the endogenous variable to cointegrate while allowing short run adjustment. The cointegrating term is known as an error correction term, as the deviation from long-run equilibrium is corrected gradually through a series of partial short-run adjustments.

In this research, VECM approach examines the equilibrium between the price of BTC index and equity market indices for five selected countries. We also use the M-GARCH to identify the changes in correlation and volatilities of BTC and Asian stock markets indices over time together with its directions positive or negative and magnitude stronger or weaker. Following that, we also analyze the time frequency domain using Wavelet investigation and balance its findings through cross-correlation and cross-coherence analysis.

Initially, it expresses how the cross-correlation between the two series varies across multiple scales, followed by the time-based evolution of the co-movement between the series along with directional leadership. Thus, the cross-coherence analysis helps us unravel new evidence on whether the higher price of BTC is influencing the prices of selected equity markets or the opposite, for instance, higher equity market prices are happening as a result of increased BTC price.

Moreover, as wavelet analysis operates in a time-frequency domain i. In the following sections, we explain in detail the estimations of the three tools. Vector error correction model VECM also called restricted VAR, is designed to use non-stationary variables that are known to be cointegrated.

VECM restricts the long run behavior of the endogenous variables to converge to their cointegrating relationship while permitting for short-run dynamic adjustment. VECM distinguishes short- and long-term associations in a model. In this content, the lagged structure of error term refers to short term deviation from long term equilibrium. The Granger causality test is also implemented in this research to understand the relationship of the dependent variables and explanatory variable.

From equation 1 , the VECM model for this study can be illustrated as the equation below:. Causality test is implemented to understand the direction of the relationship between variables. In regression modeling, the underlying theory will indicate the direction of causality between Y and X, which is in the context of single equation models, is generally from X to Y.

Hence, to understand this relationship and its directions, this research applies the Granger causality test. Second, to determine the directions and magnitude of existing correlations. This assumption is impractical in various empirical investigations. The breakdown of H t allows separate specification of the conditional volatilities and conditional correlations.

Researches in finance and economics are witnessing the growth on the usage of wavelet transformation analysis. There are some points on the aspects of wavelet methodology that suit the model of the current study. By implementing wavelet analysis, researchers are able to fetch high quality information confined into a signal in various scales.

Because the VECM analysis of this research uses the traditional econometric testing for short and long run relationship between BTC and equity market indices, wavelet testing is placed exclusively to not only perform the robustness check on VECM results, but to also explore new visions in the time-frequency domain by using cross-correlation and coherency analysis.

The former is used for interrogating signals in greater details and is, therefore, more relevant for higher frequency testing. As such, we go on to build a wavelet function by forging a series of plans including mother and father wavelets. Equation 14 is useful for capturing the degree to which two time-series co-moves across time and frequencies, an improvement over Fourier transforms which deal with frequencies alone.

Table 2 provides a description of the data used for analysis. The data is described based on mean, maximum, minimum and standard deviation. In terms of maximum values, BTC obtained higher value as compared to others. The same goes for minimum value as well in which BTC recorded minimum values.

We can observe that large dispersion occurs at BTC market where the standard deviation is of 0. The total number of observations are 1, because the data collected are in the daily basis mode. Several steps were performed, and they include the unit root test, optimal lag selections test as well as cointegration test. Table 3 shows the analysis of unit root at level and first difference.

Apparently, after incorporating the first difference, both unit root tests rejected the null hypothesis, concluding that all variables are not stationary at the level basis but it stationary in first difference. The identification of lag will be more difficult as lag variables incorporate exogenous variable in the system Ender, We conducted optimal lag selection for the indices of Asian emerging economies.

Case in hold, we can observe that each of the series is stationary, and therefore, we can evaluate whether there is the presence of long-run equilibrium or vice versa for model test in this study. To assess the existence of long-run association, we refer to Johansen—Juselius cointegration tests based on trace and maximum eigenvalue analysis.

Again, we analyze the cointegration test using the lag selected in Table 4. The trace statistics and maximum eigenvalue postulate the existence of one co-integrating vector, thus confirming that there is a unique co-integrating vector controlling the long-run association between Asian market indices and BTC. These variables are knotted together in the long run, and their eccentricities from the long-run equilibrium path would be rectified. Furthermore, the existence of co-integration also reveals the non-causality among the variables.

Table 6 shows the long-run association of market indices with BTC. The similar discussion is also applied for other Asian countries which have negative association. A possible reason to this i. Furthermore, the economic slowdown and increase in employment rate in Korea also cause Koreans to look for a better way to invest, especially digitally. The Philippines is considered a good market for BTC given the fact that the country has an active BTC community and the country has a special economic zone in which foreign crypto exchanges are licensed for operation.

Furthermore, the rise of fintech in Philippines also contributes to the rapid development of the BTC market in addition to the equity market. Table 7 discloses information on the ECT for the market indices of each Asian country. ECT basically looks at the speed of adjustment toward long-run equilibrium.

All Asian equity markets obtained negative ECT. For simplicity, Japan is selected as sample for discussion. There may be other factors which this study did not consider augmenting or detract from the disequilibrium in the next period.

We further our investigation to see the dynamic interactions between these variables. In Table 8 , we show the result of the pair wise Granger causality with a lag selected in Table 4 which is enough to whiten the noise process. From Table 7 , some general findings can be concluded. Most of the equities index do not have any short-run association with the BTC market except for Korea where there is evidence of a short-run association between BTC and Korean index.

This supports the idea that BTC investment is suitable for long-term investment instead of short-term. Lee et al. In addition, there are the plotting of estimated conditional correlations and volatilities as well. The main purpose of showing both analyses is to determine model suitability and appropriation to measure the research objective. Furthermore, all volatility decay structures are at the significant level. We also present the analysis of t-DCC model to identify the model suitability Table The maximum likelihood ML of t-DCC postulates that all volatility forecasts are statistically significant, and the estimated values are also close to one.

Thus, the discussion of analysis will be based on Table 9. A similar outcome is also applied for indexes return and shocks to the volatilities for all indexes, which are not tenacious. Let us say the volatility of any asset is obstinate given the shock in the economy; retail and institutional investors could have a chance of losing all their investment money in the long run although they have made short run profit given the uncertainty level in the BTC market.

The t-DCC result confirms that the volatilities of BTC and Asian stock indexes are not persistent and would significantly alarm investors on whether the investment is safe for them. This outcome is also consistent with a study done by Rahim and Masih Table 11 displays the result of unconditional correlation and volatilities of BTC and Asian stock market indexes.

The on-diagonal shows unconditional volatilities while off-diagonal indicates the unconditional correlation of each asset. When the unconditional volatility is closer to 0, the asset thus contains least volatility. However, if the unconditional volatility is close to 1, the asset encounters higher level of volatility, meaning higher risk. To make it clear, we sort the BTC and Asian stock indices from the highest to the lowest volatility Table Table 11 illustrates the orders of the unconditional volatilities of the five Asian stock indexes with BTC.

Interestingly, all the indexes recorded low unconditional volatilities ranging from 0. The five stock markets are less volatile whenever there is uncertainty in BTC. The BTC logged unconditional volatility of 0. STI-Singapore index recorded the lowest volatility is. In the news highlighted by DBS Singapore , Singapore investment encounters less volatility as it offers plenty of diversification opportunities to investors.

On the other hand, the other two markets i. Results of the unconditional correlation in Table are like Table 5 of the long run relationship between BTC and Asian stock indexes. The Japanese government has legalized for medium of exchange and investment purposes the BTC currency and the spillover effect has caused the share price to increase. Details of the time framework proportion are given in the footnote [ 1 ]. However, one thing is very clear, and that is BTC return is more volatile than stock indexes.

During the period of to , we can see that the volatility was not so explosive, but after , the returns of BTC are tremendously volatile. A similar sentiment is shared by Cheung et al. Comparatively, stock indexes are less volatile than BTC as revealed in Figure 1. The degree of volatilities for BTC is higher than the stock indexes during the analysis period in terms of the conditional correlation Figure 2.

Looking at the unconditional correlation, the degree of relationship can see from different time horizons. Other stock indexes observe mixed movement with BTC. For example, from until , there was inconsistent movement but with the same direction, meaning that the correlations co-move together.

As these two countries actively promote digital currency and cryptocurrency, this could be the main reason contributing to this movement. However, for STI, the correlation is most of time in the negative—positive, especially during , , , , and As mentioned earlier, STI market offers a quite diversified assets platform for investors which influences this movement.

The results of Figure 2 and Table 10 off-diagonal indicates unconditional correlation of each asset postulate a similar outcome. Thus, given the correlation between stock indexes and BTC, we can now classify which markets can be considered a safe investment or safe heaven. Generally, in finance, investors tend to opt for the negative correlation as they can diversify investment risk Jones, JPN and PHIL may not be considered as a safe zone because of the strong interdependence which would be very risky for investors.

Before we further discuss the wavelet analysis, we first need to understand their classification prior to making any judgment. Basically, the horizontal line represents the number of years while the vertical line shows the frequency component scale , with the shorter frequency range closest to the origin of the BTC return fluctuation.

Red areas show higher coherency of BTC with stock indexes while blue areas indicate lower co-movement between BTC and stock indexes. This means that the time domain co-movement is commonly weak. However, we may notice that the blue area dominates the low scale area blue color is more prevalent in the higher level of axis y , meaning that the co-movement is more obvious in the long term than it is in the short term.

Furthermore, as time progresses, the blue color is less dominant, showing an increase in the co-movement between BTC and JPN. The wavelet phase-difference specifies the dynamic relationship of return or price by looking at the lead-lag association between the paired sets example: A and B. The arrows set out the lead-lag relationship between the series. If the phase arrow points to the right, this signals that the relationship follows a positive co-movement in phase: A and B. If the arrow points to the left, the relationship is out of phase negative co-movement: A and B.

If the arrow points down, this implies the price or return of A leads to the price or return of B. If the arrow points up, then B leads A. As discussed earlier, the CWT reveals the dominance of blue which covers a large proportion of the cone, showing that the co-movement between BTC and JPN is relatively low. However, as time progresses, we can observe the increasing dominance of red, meaning that the co-movement is getting stronger short term, medium and long term.

The outcome is quite pragmatic under the scale of 64 to The lead-lag relationship postulates a positive co-movement between BTC and JPN stock index where most of the arrows point to the right. If the BTC goes up, then the stock index will also move up. This highlights that the degree of co-movement is very low even though time progresses. The red color can be observed at the scale of 16 to 64 and 64 to but at minimal level.

This happens in the medium and long periods. The difference is mainly cause by different time horizons involved. As it is very difficult to see the relationship in the short-term. As time progresses medium term onwards, thus the co-movement will be strong and the phenomena can be notice in the long-term as well. Just a small proportion of red is available under the scale of 64 to Again, the wavelet phase difference demonstrates limited arrows that point upward, meaning that the STI stock index leads the BTC Figure 5.

This can be evidenced at scales of 64 to which occur at long term. However, in the medium scale from 16 to 64, red colors some areas. Moreover, the lead-lag relationship shows the arrows pointing to the left. This is an out of phase relationship as the relationship shows a negative co-movement between BTC and PHIL during the medium term but not in the short and long periods.

Finally, for BTC and HK, again blue is dominant, which shows that the co-movement between these two remains low. As time progresses, blue remains a dominant color in the cone. Just a small proportion of red color can be observed inside the cone, which only happens at short and medium scale. A limited number of arrows are available inside the cone which can predict the wavelet phase difference. The arrows are available only at the medium scale, such as 16 to The outcome shows that in the short term, the arrow points to the right while in the long term, the arrow points to the left.

Basically, there are in-and-out phases for the lead-lag relationship Figure 6 and Figure 7. The objective of this paper was to investigate the connection between BTC with Asian Stock Indexes from July 20, until April 26, in terms of long and short run relationships, correlations, volatilities and time frequency domains as well as lead-lag analysis. We also observed that in the short-run, only one market had a relationship with BTC movement i.

This reveals that BTC investment is suitable for long term investment rather than short term considering this aspect. Indirectly, Singapore investment encounters less volatility among other markets. In addition, we also observed that three markets had a negative correlation with BTC i. These findings are consistent with the prediction made by VECM. Based on the time-frequency domain analysis using the Wavelet, we observed the following outcomes: In the long run, BTC and JPN shared strong co-movement, while the lead-lag predicted a phase category.

BTC, KOR and STI postulated a similar outcome in which the co-movement between the markets was very low and the lead-lag analysis showed an anti-phase relationship. The co-movement between BTC and PHIL existed in the medium scale quite significantly but the lead-lag relationship revealed an out-of-phase category. BTC and HK revealed a low co-movement and mixed lead-lag relationship.

Our findings have implications for academic and industry practitioners. Theoretically, our study enriches the existing literature available in the area of cryptocurrency, especially because it uses advanced econometrics techniques. Most of the results are consistent and show the different dimensions about long and short run relationships, volatilities, correlations and time-frequency analysis.

We have also used Asian emerging economies which are rarely the subject or focus in the literature as existing studies are more skewed toward the West. As for industry practitioners, we believe the outcomes of this study will be a significant sway for retail and institutional investors to design better strategies on diversifying their stock portfolios with different holding period horizons and dimensions as mentioned above.

However, it must be remembered that, not all risks can diversify as markets are still expose to the systematic risks. Future researchers are encouraged to test these phenomena with other developing countries to see if similar patterns are observable as to that of advanced economies. Comparing Asian countries with developed countries will certainly add value to the existing literature with large data sets. Conditional volatilities of bitcoin with Asian Indices.

Conditional correlations of bitcoin with Asian Indices.

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An American nonprofit called the Bitcoin Foundation was founded in to support the development and adoption of the Bitcoin protocol. After three years, however, the foundation eventually ran out of cash and was dissolved. In , Adam Back, another cypherpunk and the inventor of Hashcash — a cryptographic hashing algorithm created in which used the same proof-of-work mechanism that Bitcoin would later adopt — co-founded Blockstream.

Blockstream is a for-profit tech company that develops new infrastructure on the Bitcoin network, including Lightning Network and sidechains. World currency prices are based on rates obtained via Open Exchange Rates. Both countries have increased regulation in recent months, creating a less nurturing environment for the crypto industry; most major cryptos were in the green in Wednesday trading.

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Mining and transacting cryptocurrencies, such as bitcoin, do present energy and emissions challenges, but new research shows that there are possible pathways to mitigate some of these issues if cryptocurrency miners are willing to operate in a way to compliment the deployment of more low-carbon energy. One way to invest in Bitcoin that has a positive effect on renewable energy is to encourage mining operations near wind or solar sites.

This provides a customer for power that might otherwise need to be transmitted or stored, saving money as well as carbon. Currently, projects are under development, but the issue of overgenerated wind continues to exist. By harnessing the overgenerated wind for Bitcoin mining, Wyoming has the opportunity to redistribute the global hashrate, incentivize Bitcoin miners to move their operations to Wyoming, and stimulate job growth as a result.

Rochester, NY. In responding to these pressures and events, some miners are providing services and innovations that may help the viability of clean energy infrastructures for energy providers and beyond, including the data and computing industry. The paper finds that if Bitcoin loses legitimacy as a store of value, then it may result in lost opportunities to accelerate sustainable energy infrastructures and markets.

Parvez 1 November The main problem facing the PV power plants deployment is the intermittency which leads to instability of the grid. Renewable and Sustainable Energy Reviews. Windfarms can hedge electricity price risk by investing in Bitcoin mining. The enormous energy demand from Bitcoin mining is a considerable burden to achieve the climate agenda and the energy cost is the major operation cost.

On the other side, with high penetration of renewable resources, the grid makes curtailment for reliability reasons, which reduces both economic and environment benefits from renewable energy. Deploying the Bitcoin mining machines at renewable power plants can mitigate both problems.

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