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All markets show close movements of volatilities except USDT which acts differently from the others over the whole period. One of the major reasons for this smooth volatility in the USDT market was the herding behavior of investors toward the markets where their returns were relatively much higher. Therefore, the USDT market does not seem to be integrated with the other markets.

Figure 4 shows the conditional correlation between the cryptocurrency markets. The figure depicts the variations in the results of the conditional and unconditional correlations of cryptocurrencies. The lines representing the correlation of pairs cryptocurrency market of USDT with the other markets are mainly at the bottom crossing, with lines equal to zero showing less correlation.

In other words, the returns from the USDT market show less correlation with the other selected cryptocurrency markets. The graphical representation of these markets shows close movements of the correlation lines, suggesting that these four popular markets behave in a similar way.

As supported by the values of unconditional volatility, investors tend to buy highly demanded assets in the cryptocurrency market in parallel with a reduction in diversification benefits. However, financial markets mostly follow a cyclical and iterative pattern, indicating that investors act independently over time. In this regard, GARCH family models do not fully grasp the information of all time scales, although they control the conditional correlation and covariances.

Therefore, the next step was to use various wavelet analyses to assess the changing relations that occur across different time scales, rather than the given time scale. The results imply that most of the power is concentrated within the last quarter of and the early phase of , although there is appreciable power over longer periods. With wavelet power coherence analysis, one can see variations in the frequency of occurrence and amplitude of the COVID outbreak. For instance, the global lockdowns and the slowdown of production can be implemented as one of the core factors to understand these high frequencies through the cryptocurrency markets.

It also indicates that these markets were highly volatile at that time. Further, looking at the power spectrum, it is clear that the volatilities dropped from high frequency low-scales to low frequency high-scales during the second lockdown effect. This shows the market fluctuations were highly influential on financial investors of the asset investment horizons.

The second useful investigation was the wavelet coherence, defined as the square of the cross-spectrum, normalized by the individual power spectra. It shows how much the linear information of one asset is explained by the other, and thus, it can be used to estimate causality among the selected assets. Also, the outputs of the conditional correlation graph in Fig. From the wavelet coherence plot, it is confirmed that the initial correlations among the three assets exist between 16 and 32 days, showing that there is a significant co-movement between short-term investors.

However, as the cryptocurrency markets mature, the return co-movement becomes intense. In particular, the increase in depth of return co-movement became permanent from just after the first lockdown of March up to the present with a significant coherence up to days, implying a possible increased scale of long-term investors. Furthermore, this study uses cross-spectrum analysis to investigate the nature of volatility spillover across different time horizons. In particular, it allows for an estimation of the coherence between different assets, indicating how much linear information is transferred from one to another at each frequency.

Meanwhile, the estimation of the interaction strength between the selected cryptocurrencies is provided using wavelet cross-spectrum. From the plots represented in Figs. The heat map from blue to red indicates an increasing strength of correlation between the markets. Blue represents a weak connection, while red denotes a strong relationship among the selected assets. The same pattern applies to the WPS and wavelet coherence plots.

One of the most distinguishing features of the evidence from the wavelet cross-spectra is the highest correlation between the selected markets and the others. However, the scale of that relationship is very decisive among the three even though the cross-correlations from those selected markets are highly significant with the others.

From the wavelet cross-spectrum, the spillover between the selected cryptocurrencies was found to be infrequent prior to the second half of October , which was the beginning of the second global lockdown and only exists in the short-run up to 8 days.

In the latter period, while the spillovers remain; they become more pronounced along with high frequency. Therefore, from the analyses of conditional volatilities and wavelet cross-spectrum, it can be concluded that the volatility spillover between those markets becomes more significant at the second lockdown compared to the first and it is limited in the short-run.

Results from the three different but integrated models imply that financial investors tended to move to assets where the prices of those assets were biased toward an increase in line with leading a surge in volatility transmission among the cryptocurrencies. Therefore, the economic implication is that the financial investors were substantially exposed to herding behavior during the COVID pandemic.

This can be considered as a critical way to understand why and how financial investors follow the same kind of behavior at the time of economic problems that arise from events such as the COVID pandemic or global recessions. However, the main problem is the potential surge in risky behaviors in these markets, in line with an increase in herding behavior.

Since many investors follow the same behavior in these markets, they also initiate an increase in the volatility rate for such assets, in which their prices rose rapidly over a short time. Two integrated methods were implemented to identify any possible speculative bubble in selected digital currencies: 1 the value-at-risk VaR and 2 the conditional value-at-risk CVaR.

The technical reason to use those methods is to measure the level of financial risk within cryptocurrencies over the COVID pandemic, and thereby, to formulate an idea about potential explosive behavior. In other words, the potential for loss in different markets is evaluated through the implementation of VaR—which represents a worst-case loss associated with a probability and a time horizon—and CVaR—which is the expected loss if the worst-case threshold is ever crossed—methods, and thus, the possibility of occurrence for defined loss is determined to quantify the level of financial risk.

The economic reason for detecting price movement over the selected time horizon was to provide an initial understanding of the safe haven characteristics of these digital currencies. These facts provide initial evidence that digital currency investments may increase portfolio risk rather than acting as a safe haven resulting in a surge of speculation across the cryptocurrency market. Over the entire period considered, an investor in these digital assets would have many opportunities to increase their wealth relative to their investments in stock markets.

In addition, the analysis covers the CVaR model to quantify the expected shortfall to measure the likelihood of loss exceeding the value-at-risk. The estimations also indicate the same pattern as that found in VaR modeling, in which financial risk is much higher in digital assets than in stock market indices.

Overall, these findings show that investing in selected digital currencies may result in increased financial risk, discrediting the safe haven hypothesis for the cryptocurrency market. First, we identified Bitcoin as the core market in terms of the demand scale. The EGARCH model was utilized to detect the conditional variance of the closing prices of selected assets and to capture the leverage effects of shocks in terms of the relationship between shocks to variance and shocks to returns.

We found that positive shocks had a greater impact on volatility than negative shocks e. Therefore, in these markets none of the volatility asymmetries indicates that financial investors are more sensitive to positive news i. The maximum likelihood estimates of the Gaussian DCC model of cryptocurrencies show that volatilities could be mainly explained by their fluctuations.

Further, the correlation structure between the selected asset pairs strengthened during the moment of shocks, especially for Bitcoin, Ethereum, and Litecoin prices, implying investor panic. This means that the COVID pandemic has led to more integrated cryptocurrency markets, and thus has also stimulated herding behavior among financial investors.

The final step was to apply the multiscale correlation technique using wavelet methods, which capture information across different frequencies without losing information from the given time horizon. Using these methods provided a way to investigate the relationship between various assets at different time scales and frequency bands by capturing the low- and high-scale effects of any shock that occurred within and across financial markets.

We examined three types of wavelet methods, namely wavelet power spectrum, wavelet coherence, and wavelet cross-spectrum. The common point of these measures is that the possibility of the selected cryptocurrency markets to a large extent is significant in the short run, especially as depicted in the wavelet coherence analysis.

In addition, from the wavelet cross-spectrum, it is found that volatility spillover is relatively high for three major cryptocurrency markets, namely Bitcoin, Ethereum, and Litecoin, which was exacerbated after the second lockdown effect in November , but persisted up to days.

However, the others reflected a moderate spillover in terms of their volatility and existed only in the short run, up to 8—16 days. The empirical findings imply that cryptocurrency markets are largely assumed to be one of the core financial platforms where financial investors increasingly participated for higher returns during the COVID outbreak.

However, it led to three major outcomes: 1 increased levels of risky investments, 2 a greater level of herding in financial markets, and 3 exacerbating the nature of volatility spillover. While a moderate level of volatility offers a certain number of advantages to financial investors to diversify their assets, it poses some problems because the cryptocurrencies have no intrinsic value, and they do not offer any dividends or a specific returns.

Therefore, these markets face notable concerns over circumstances such as legal position, safety, and transparency. Considering the apprehension about the ongoing increase in the prices of such cryptocurrencies together with unfettered demand, the increasing uncertainty and the level of risk may cause the dynamics of volatility spillover to change over time.

In conclusion, increased unfettered demand for some major cryptocurrencies may lead to a serious loss of returns for many financial investors, resulting in a sudden unexpected decrease in prices. Although the empirical findings confirm that the volatility spillover in cryptocurrency markets is a widespread issue, there are some limitations to this study.

Primarily, the methodological framework restricts the implementation of additional factors in empirical analysis. Therefore, following the general trend in the existing literature, empirical estimations are limited to some degree. In addition, although the presence of volatility spillover for the three major cryptocurrencies Bitcoin, Ethereum, and Litecoin are validated under the implementation of current methods, the reasons behind the volatility spillover can be estimated based on observations.

This fact can also be generalized for other cryptocurrency markets used in the empirical analysis. Another critical limitation concerns the existing literature in which the studies are not extensive in terms of their role of exploring explosive behaviors on the volatility of digital asset markets during the COVID pandemic.

This restricts us from comparing the empirical results found in this study with those of others. Finally, the data selection process did not proceed with rule-bound data because the series were selected based on a comparison technique by looking at alternative data. However, even though the data selection process did not adhere to the rules, the empirical findings pursuant to the use of prices of selected cryptocurrencies that are derived from the alternative datasets validate the current results in the context of robustness checks.

In light of these limitations and drawbacks, potential future directions for research are based on two things: first, future studies will be expanded using alternative methodologies, considering the presence of speculative-led behaviors and volatility spillovers in cryptocurrency markets during the COVID pandemic.

Second, future studies will use alternative models in which other volatility-based potential factors can be integrated into the analysis to detect reasons for a surge in speculative motives among investors and thereby volatility spillover. For more information on opinion dynamics in finance and business, please see Zha et al. Following the arguments proposed by Alexander and Dakos , the robustness check for the analysis using alternative data derived from different data sources such as Goldprice, CoinMarketCap, Coinbase, and Investing was implemented towards the use of same models.

However, the results were not changed to a large extent. Three cyrptocurrencies i. In that vein, the study is based on Coindesk data since the results were not substantially changed. The second reason to select Coindesk data is based on the fact that this data source instantly reveals the change in prices of cryptocurrencies and thus it transmits a huge amount of information for the researchers. This study considers one of the effective stablecoins, namely the Tether, to show that the valuations of stablecoins are supposed to be less volatile than other digital assets, since they are pegged directly to a fixed, non-virtual currency.

Therefore, in consideration of the empirical findings, it can be argued that if the most volatile cryptocurrencies are pegged to an economically and financially strong economic units, some major problems that may emerge in those markets can be slightly decreased in the long-run.

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Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all SpringerOpen articles Search. Download PDF. Abstract This study investigates the dynamic mechanism of financial markets on volatility spillovers across eight major cryptocurrency returns, namely Bitcoin, Ethereum, Stellar, Ripple, Tether, Cardano, Litecoin, and Eos from November 17, , to January 25, Introduction Triggered by the recent rapid rise in the price of cryptocurrencies such as Bitcoin, Ethereum, and Litecoin, a lively interest in research on these explosive bubbles has emerged as to whether the COVID pandemic stimulated risky behaviors among financial investors.

Literature review The rise in the value of cryptocurrencies in the digital asset market has promoted a number of studies aimed at investigating the dynamics of that market along with listing the major influence of channels.

However, the currently relatively large and varying number of strikes and expiries entering the index increases complexity and, thus, makes it harder to physically replicate the index. That being said, decreasing the number of assets by focusing on fewer nodes would increase concentration risk and make the index more susceptible to manipulation.

Considering that the young cryptocurrencies derivative market cannot provide the liquidity that is available on mature markets, we decided in favour of diversification over a larger number of strikes and expiries. This assessment might be updated once the liquidity situation for cryptocurrency derivatives improves.

Britten-Jones and Neuberger pioneered a risk-neutral variance forecast that does not rely on a specific model, but only on market prices for option contracts. Their method is applied to most modern volatility assets, such as variance swaps or the VIX volatility index family. The realized volatility is conventionally calculated on closing price log-returns.

Carr and Lee call this replicating portfolio, which only requires a static position in options 12 and dynamic position in the underlying asset, a synthetic variance swap. As laid out in detail by Demeterfi et al. Note that Eq. The following Riemann sum approximates Eq. The ATM adjustment therefore accounts for the distance between the forward level F and the first strike below the forward level K n.

This is, the forward adjustment shifts the strikes to their required at-the-money levels. In a final step, these variances have to be interpolated over their respective maturities, because the CVX is designed to reflect the at-the-money volatility for exactly 30 days.

Using the interpolation from Eq. Despite its popularity, the literature discusses shortcomings of the method, especially in lieu of heavy-tailed markets. Similarly, Chow et al. However, for the purpose of this paper, comparability to existing benchmarks outweighs technical improvements. In the simplest of option pricing models, volatility is the only free parameter that is not observable on the market. Once market prices for options become available, one can use said models to extract implied information by solving for volatility.

Madan et al. For the purpose of a volatility index, however, it is paramount that the only information extracted, i. This excludes many advanced option pricing formulae and brings us back to plain vanilla Black—Scholes type models. More specifically, this paper uses the Black model to compute the implied volatility from a market of cryptocurrency options and futures. Extracting implied volatility from a market of option prices is not possible in closed form, hence, a Newton—Raphson NR algorithm is used for the job.

The NR algorithm is used to compute the volatility surface for each timestamp in the sample. The index is designed to represent the implied volatility of a 30 days to maturity at-the-money option. The surface is interpolated accordingly by inverse distance weighting as introduced in Eq.

The first and foremost shortcoming of this method is the model itself, which requires a number of limiting assumptions. Most importantly, the Black 76 model assumes normally distributed log-returns, an assumption that is not warranted for financial assets in general and cryptocurrencies in particular.

Data are available and kept up-to-date at www. We collect data snapshots in 5 min intervals for all option and future contracts that are traded on Deribit. The data are available from February 6, until July 6, VIX: is designed to measure day expected equity volatility. RVX: is a measure of day expected equity volatility similar to VIX, however, with Russell index options as underlying.

VVIX: measures expected volatility of the day forward price of the VIX, and hence, the index represents the volatility of volatility. GVX: is designed to measure day expected volatility of gold prices. SRVIX: 1-year implied volatility of the year swap rate. The index is calculated from 1-year swaptions on year USD interest rate swaps.

A concise overview is provided in Table 2. VCRIX is calculated from return data, whereas all other indices are based on option price data. Siriopoulos and Fassas provide a more complete overview of volatility indices for traditional financial assets. CVX is the model-free annualized expected volatility over the next 30 days, which is based on mid-prices for Bitcoin options see Sect.

CVX76 is based on the Black 76 model implied volatility and interpolated from a volatility surface for each timestamp in the data see Sect. Both indices are based on the same option data. Annualized day expected volatility for Bitcoin in hourly frequency. This is a typical observation for volatility indices. On one hand, market corrections are typically more rapid than upward moves and cause spikes in volatility.

On the other hand, with a net long position over all investors in the underlying, the demand for downside protection drives volatility prices especially when markets are falling. The black line indicates a log-normal left and normal right distribution fit. A rug plot just above the x-axis indicates the frequency and clustering of observations.

One can clearly see the two main clusters of observations for CVX, i. Gaussian kernel density estimate and rug plot blue. Left: CVX with fitted log-normal distribution. Right: CVX log-differences with fitted normal distribution color figure online. Similar dynamics can be observed for our cryptocurrency volatility indices. This has interesting implications, especially for the CVX76, as the index methodology relies on the assumption of normally distributed log-returns in the underlying, which is frequently challenged by strong market movements.

More specifically, when comparing the index data of CVX and CVX76, one can see that the indices are more similar during less volatile times and vice versa. We want to further investigate these joint dynamics before returning to the analysis of cryptocurrency volatility.

Differences between CVX and CVX76 are obviously owed to the fundamentally different calculation methods; however, both methods are in principle designed to quantify the same information, i. We present evidence that both indices are cointegrated and that CVX76 tends to return to the level of CVX—not vice versa—which supports the argument that.

In a nutshell, out-of-the-money option prices, especially during strong market moves, are higher than suggested by a light-tailed normal distribution. This effect is compensated by a particularly high Black implied volatility. Recall that the CVX76 is constructed as a measure for at-the-money volatility, by interpolation over a range of strikes, including out-of-the-money options. We postulate that both indices share a strong relationship that is sometimes distorted, especially during large movements in the underlying, but subsequently corrected.

This claim is corroborated by the following cointegration analysis. First, we use the Augmented Dickey—Fuller ADF test 17 to confirm that the differences as well as the log-differences of CVX and CVX76 are stationary, and hence, the two time-series are integrated of order 1, in the sense of cointegration.

Second, we estimate the cointegrating regression. A t-statistic of 5. The strong relationship implies that distortions to the long-term equilibrium of both indices are temporary and correcting over time. The adjustment behaviour can be analyzed by estimating the underlying error correction model. The error correction model is specified as. If the error is 0, the model is in equilibrium and vice versa. Finally, we want to estimate how long it takes for an existing error to be reduced by half, i.

Recalling that the indices are calculated in hourly frequency leaves us with a half-life of roughly 14 h. Swapping the indices in Eq. Changes in the spread between both indices, i. That is, the indices diverge in markets where a normal distribution is not able to reflect the actual price movements, i.

A similar tail-risk metric, which is based on GARCH models with normal and heavy-tailed innovations, has previously been applied to construct tail-risk protection strategies Packham et al. First, there exists a strong correlation between the 76 spread and negative tail returns 18 see Fig.

Second, there exists a positive correlation, yet less strong 0. However, additional tests would be necessary to further substantiate this claim in the context of Cryptocurrencies. Bitcoin returns and value at Risk. Compared to classical asset volatilities , cryptocurrency volatility dynamics are often disconnected, yet, share common shocks.

Figure 6 shows that Bitcoin was slow to react to the overall market distress that was caused by the COVID crisis, which transmitted to cryptocurrencies roughly 30 days after traditional assets already experienced a sharp increase in expected volatility. The results are also similar to the paper on realized volatility from Conrad et al. It is based on price data and constructed using a heterogeneous autoregressive HAR model. As such, the index methodology is fundamentally different from our implied volatility indices, which are based on option prices.

In contrast, all other implied volatility indices considered in this paper are not indexed but rather provide an ad-hoc value for implied volatility. The indices in Fig. The heat-map in Fig. The correlation between Bitcoin and other volatility ranges roughly between 0. The disconnection from the dynamics of traditional markets supports claims on the potential for portfolio diversification made by, e.

Heat-map of Pearson correlations between daily log-differences of implied volatility indices. Cryptocurrency volatility appears disconnected from the cluster of traditional asset correlations. It is well established that asset return correlations are not constant over time and may be strongly affected by specific events, hence, present an important risk factor.

This has important implications for hedging. In a nutshell, the prices for hedging increase when protection is needed most. This makes the somewhat disconnected dynamics of cryptocurrencies particularly interesting. The current research that attributes great diversification potential to crypto-assets is based on the presumption that these assets remain an exotic asset class with dynamics that are separated from traditional markets.

While this claim is currently supported by the data, increasing acceptance of this market might drive overall market integration in the future and in turn bring crypto market dynamics closer to traditional assets. The COVID crisis already showed that cryptocurrencies, despite their delayed response, are subject to systemic volatility shocks.

Box-plots in Fig. The first is naturally an extremely volatile asset class. The latter recently saw its highest levels since inception in , which was primarily driven by the oil price war between Russia and Saudia Arabia and is therefore of no further interest to this study. Since the invention of Bitcoin, cryptocurrencies have evolved into a new class of financial assets.

Naturally, as cryptocurrency spot markets evolve, markets for derivatives thereon follow. Of those, option markets offer the unique potential to extract volatility information that would otherwise be unobservable. Volatility is an important metric and the most common risk measure in finance.

Accessing stable and reliable volatility information is of fundamental interest to investors and risk managers alike. However, implied volatility must be based on a broad spectrum of liquid and reliable option prices, and hence, requires a much larger data foundation than realized volatility.

Our method addresses liquidity concerns for this young asset class by broadening the base of relevant options, when compared to volatility benchmarks for traditional assets e. Given this method, we find that the liquidity on cryptocurrency option exchanges is sufficiently developed to produce stable results.

Comparing the volatility dynamics captured by CVX to traditional volatility benchmarks, we observe that cryptocurrencies live a somewhat secluded life and therefore bear diversification potential, a finding that is in line with the literature. However, despite a lag, the COVID crisis is a good example for a global shock that affects cryptocurrencies and traditional assets alike. This is additional evidence on the limits of diversification during times where it is needed most. However, due to the assumption of normally distributed log-returns in the Black method, the CVX - CVX 76 spread is an interesting indicator of market implied tail-risk.

More specifically, the two indices share the strong statistical bound of cointegration, which is temporarily distorted during heavy-tailed markets. An error correction model shows that said distortions have an average half-life of roughly 17 h. Cryptocurrency option liquidity is centred on Bitcoin, which is currently a limit to the accessibility of cryptocurrency volatility.

Until liquidity spreads out to other assets, Bitcoin has to be used as a surrogate for the entire asset class. Preferably, a liquid option market on an index such as the CRIX could be used in future to significantly improve the scope of the CVX, without the risk of fragmented liquidity in the underlyings.

See www. For a multi-dimensional interpolation volatility surface , this case becomes even more unlikely. Only the least significant test statistic out of all settings is reported in the paper. Figure 5 shows a historical simulation HVaR and delta-normal VaR ; both methods are standard in the literature and we refer to Jorion for technical details.

The author would like to thank Carol Alexander, Arben Imeraj, and Andreas Weber for helpful comments and discussion as well as two anonymous referees for their valuable suggestions that helped to improve the paper in several respects. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Digit Finance. Published online Aug 2. Fabian Woebbeking. Author information Article notes Copyright and License information Disclaimer.

Goethe University Frankfurt, Frankfurt, Germany. Fabian Woebbeking, Email: ed. Corresponding author. Received Nov 2; Accepted Jul 3. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Introduction Since Nakamoto proposed Bitcoin as a peer-to-peer electronic cash system, this and other cryptocurrencies 1 have evolved into a new class of financial assets. Markets and conventions Before formalizing the index and its rules, this section reviews the underlying market of cryptocurrency derivatives. Table 1 Option contract specifications. Open in a separate window. Index methodology and rules The fundamental idea of volatility indices dates back to Brenner and Galai , who envisioned financial instruments for the hedging of volatility changes.

Selection of option contracts and aggregation To ensure that only qualified market prices enter the index, all entries with a trading volume of 0 and or without a mid-price are excluded. Model free volatility CVX Britten-Jones and Neuberger pioneered a risk-neutral variance forecast that does not rely on a specific model, but only on market prices for option contracts.

Model implied volatility CVX76 In the simplest of option pricing models, volatility is the only free parameter that is not observable on the market. The cryptocurrency volatility index CVX Data are available and kept up-to-date at www. Data We collect data snapshots in 5 min intervals for all option and future contracts that are traded on Deribit. Table 2 Volatility indices used in this paper. VCRIX is calculated from return data, whereas all other indices are based on option price data Siriopoulos and Fassas provide a more complete overview of volatility indices for traditional financial assets.

Conclusion Since the invention of Bitcoin, cryptocurrencies have evolved into a new class of financial assets. Declarations Conflict of interest The authors declare that they have no conflict of interest. Footnotes 1 For an introduction to Cryptocurrencies, i. The influence of bitcoin on portfolio diversification and design. Finance Research Letters. The bitcoin vix and its variance risk premium. The Journal of Alternative Investments.

Bitcoin: currency or investment? Available at SSRN Black F. The pricing of commodity contracts. Journal of Financial Economics. Tails, fears, and risk premia. The Journal of Finance. Spillovers between bitcoin and other assets during bear and bull markets. Applied Economics.

Does bitcoin hedge global uncertainty? On the hedge and safe haven properties of bitcoin: Is it really more than a diversifier? New financial instruments for hedge changes in volatility.

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How to mine diamond cryptocurrency | This paper considers two kinds of volatility: first, historical volatility that is calculated from previous prices of the underlying; second, implied volatility that is cryptocurrency volatility data from current market prices of options. In the past, investors had no alternative to VIX options and futures for managing volatility exposures as measured by VIX levels. VVIX: measures expected volatility cryptocurrency volatility data the day forward price of the VIX, and hence, the index represents the volatility of volatility. We collect data snapshots in 5 min intervals for all option and future contracts that are traded on Deribit. This compensation may impact how and coin cryptocurrency listings appear. Technol Forecast Soc Change |

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Media outlets, influencers, opinionated industry moguls, and well-known cryptocurrency fans create investor concerns, leading to price fluctuations. Is Bitcoin Safe to Buy? Article Sources. Investopedia requires writers to use primary sources to support their work. These include white papers, government data, original reporting, and interviews with industry experts. We also reference original research from other reputable publishers where appropriate.

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Max Supply. Total Supply. Buy Exchange Gaming Earn Crypto. Crypto Volatility Token Links. Polygon 0x9cd Crypto Volatility Token Contracts. Please change the wallet network Change the wallet network in the MetaMask Application to add this contract. I understand. Here are some other articles that you may be interested in: What Is a Crypto Faucet? What Are Crypto Debit Cards? What Is Web 3.

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Bitcoin price volatility Data points used, ,, Price volatility is calculated as standard deviation from all market trades. Baur and Dimpfl () did an in-depth analysis of Bitcoin realized volatility and showed that the volatility of Bitcoin is extreme compared to major fiat. Acheson's own analysis shows the average day and day volatility for Bitcoin in has been higher than in all except two of the past.