The Cryptocurrency Volatility Index (CVI index) has been introduced to estimate the 30-day future volatility of the cryptocurrency market. In this article, we introduce a new Deep Neural Network with an attention mechanism to forecast future values of this index. We then look at the stability and performance of our proposed model against the benchmark models widely used for time series prediction. Furthermore, we show that the well-known Simple Moving Average method, while it has its own advantages, has the weak spot when dealing with time series with large fluctuations. Granted, Bitcoin still holds considerable dominance over the cryptocurrency markets. Nevertheless, this dominance appears to be shrinking as the crypto markets grow more diversified.
Specifically, investors are generally more likely to trade and settle their upcoming investments on a Friday, seeing as the stock market will be closed in the coming days. Saturdays, on the other hand, are the least volatile days for Bitcoin trading. This can likely also be explained by the fact that investors do not traditionally make big moves during weekends, as the traditional markets are closed during this time. A very important benchmark and investment tool are financial indices, which allow investors to obtain information on the current state of the market. Furthermore, indices that are turned into tradable assets and derivatives thereon improve market accessibility.
What is Volatility?
As the crypto market continues to evolve, embracing volatility will become increasingly important. Staying informed, adapting to market conditions, and maintaining a long-term perspective can help traders make the right decisions. Technical analysis is also a popular approach used by traders to predict and manage volatility in the cryptocurrency market. Various technical analysis tools can assist in identifying patterns, trends, and potential price movements. News, social media, and trader sentiment can heavily influence the demand and supply dynamics of cryptocurrencies, leading to volatile price movements.
This is a great position to be in as it is much more lucrative than simply providing liquidity for a small fee and being at risk of impermanent loss. Past that, volatility creates opportunities for traders looking to make a profit by buying and selling assets. The NR algorithm is used to compute the volatility surface for each timestamp in the sample. This leaves us, for every point in time t, with a surface of implied volatilities σ(τ,K) that spans over all strikes K and maturities τ of the available options. Nevertheless, it is important to make a significant distinction when talking about volatility among cryptocurrencies. Specifically, this distinction is to separate crypto volatility and Bitcoin volatility.
Strategies for Trading Crypto During Volatile Phases
However, the accuracy seems to fall off gradually as the sliding window size increases, the first sliding window sizes perform better than all other benchmarks with the exception of our method (AT-LSTM-MLP). SMA with sliding window size of 2 yields the best result of 2.02, 2.63, and 2.16 in MAE, RMSE, and SMAPE, respectively. Particularly, SMA only works well with stable data, i.e. when the difference between Non-deliverable Forward Ndf time stamps within a sliding time window is small. Nevertheless, volatility indexes like these are nearly useless without the underlying knowledge to understand them. Be sure to check out Ivan on Tech Academy, the best place to learn about blockchain, to get all the basic knowledge to become a trading pro. What’s more, our blog is updated with free, in-depth articles like this one on a daily basis.
The two resulting volatility indices are cointegrated and the corresponding error correction model can be utilized as a metric for market implied tail-risk. Many of the reasons for price volatility in mainstream markets hold true for cryptocurrencies as well. News developments and speculation are responsible for fueling price swings in crypto and mainstream markets alike.
Severity of price fluctuation
In other words, if it’s all crypto doom and gloom on TikTok and X, expect downward volatility swings. In the crypto space, users call this ‘buying the dip’ and ‘taking profit’ — in other words, as volatility accompanies the crypto market, one can wait for a price dip to buy and often sell on a high soon after. Understanding volatility is crucial for anyone involved in the cryptocurrency market. By grasping the concept of volatility, traders are able to make more informed decisions and mitigate potential risks.
Section 3.1 lays out general index rules, such as option selection criteria and the interpolation method. Those index rules are designed to be as similar to existing volatility indices as possible, while accounting for the specifics of cryptocurrency markets. Sections 3.2 and 3.3 introduce two alternative volatility measures that are suitable for the index.
Volatility and Liquidity in Cryptocurrency Markets—The Causality Approach
If we assume the cryptocurrency industry will continue to grow more diversified, crypto volatility could diverge further from Bitcoin volatility. As the name indicates, Bitcoin volatility technically refers to the price volatility of Bitcoin. On the other hand, crypto volatility can be seen as the overall volatility of the crypto market. It is easy to see how differentiating between the two will potentially become more important in the future. As Bitcoin’s industry dominance appears to be waning, this distinction potentially becomes even more significant.
Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings. Every element of the crypto sector is new and evolving daily, so it makes sense to approach cryptocurrencies with a degree of caution as well as excitement. If a trader expects that some large-scale shock can affect the whole market, he can buy CVI and, if the market downturn really happens, the trader can make substantial gains from the trade. Cryptocurrencies have revolutionised the financial landscape with their decentralised and digital nature. However, they also come with a characteristic that typically influences them more than most fiat currencies — volatility.
VIX (volatility index)
This is defined as a measure of the 30-day future fluctuation degree of the price of the entire cryptocurrency market using the Black-Scholes option pricing model. In this way, an index that fluctuates between 0 and 200 is developed, such that 200 will indicate the maximum level of implied volatility in the market whilst a value of zero indicates the lowest volatility [10]. This index is intended to prevent investors from putting themselves at risk by modifying their trading strategy in line with different values of CVI. The higher the CVI value is, the greater the risks are but also the greater the potential return is. Cryptocurrency option liquidity is centred on Bitcoin, which is currently a limit to the accessibility of cryptocurrency volatility.
Initially, the $GOVI token was airdropped to $COTI holders and can only be claimed by using the CVI platform.
Options—like other financial derivatives—are tied to their underlying by an arbitrage relationship, which is based on the replication of the options’s cash-flow.
Since Nakamoto (2008) proposed Bitcoin as a peer-to-peer electronic cash system, this and other cryptocurrencies1 have evolved into a new class of financial assets.
Nevertheless, this dominance appears to be shrinking as the crypto markets grow more diversified.
News, social media, and trader sentiment can heavily influence the demand and supply dynamics of cryptocurrencies, leading to volatile price movements.
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.
LSTM comes into second place with errors at more than double comparing to AT-LSTM-MLP. Whereas, the three remaining methods show poor results as the predicted values are too far from real values. These results give an answer to our research question that AT-LSTM-MLP can predict the future value of the CVI index well. Simple methods based on statistical learning frameworks have been found to show good performance in many studies, e.g. Simple Moving Average (SMA) [2], Support Vector Regression (SVR) [11] and Random Forest (RF) [32].
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On the other hand, the emergence of the derivative market has signaled the need for solid pricing strategies as well as reliable risk measures. There is a growing need for a new decentralized volatility index that provides a proper estimation of the risk measurement of the cryptocurrency components, and a delivery of market status information to potential investors. Government regulations or policy changes can affect how cryptocurrency can be used and is viewed, leading to increased volatility. A recent example is the approval of spot Bitcoin exchange-traded funds (ETFs) in the US, which led to billions of US dollar inflows into the funds and price volatility. The results from this study contribute to literature on the cryptocurrency market with some useful tools and information that aim to helping the investors in making decisions in investment. We believe that our method can be applied to other prediction tasks that involve time series because of its good performance.
This is, since cryptocurrency options were introduced in 2016, market liquidity and participation has improved significantly. According to data from Skew2, the total number of outstanding contracts (open interest) has more than tripled from its 2019 value, reaching a market size above USD 1 billion for the first time in mid-2020. This surge in size provides a great opportunity to tap a very interesting source of volatility information. Nevertheless, our volatility indexing method addresses remaining liquidity concerns for this young asset class, ultimately allowing us to extract stable cryptocurrency volatility information. According to data from SkewFootnote 2, the total number of outstanding contracts (open interest) has more than tripled from its 2019 value, reaching a market size above USD 1 billion for the first time in mid-2020. At its core, CVI tracks the 30-day implied volatility of major cryptocurrencies, namely Bitcoin and Ethereum.
What Is Volatility? Understanding Market Swings
The Cryptocurrency Volatility Index (CVI index) has been introduced to estimate the 30-day future volatility of the cryptocurrency market. In this article, we introduce a new Deep Neural Network with an attention mechanism to forecast future values of this index. We then look at the stability and performance of our proposed model against the benchmark models widely used for time series prediction. Furthermore, we show that the well-known Simple Moving Average method, while it has its own advantages, has the weak spot when dealing with time series with large fluctuations. Granted, Bitcoin still holds considerable dominance over the cryptocurrency markets. Nevertheless, this dominance appears to be shrinking as the crypto markets grow more diversified.
Specifically, investors are generally more likely to trade and settle their upcoming investments on a Friday, seeing as the stock market will be closed in the coming days. Saturdays, on the other hand, are the least volatile days for Bitcoin trading. This can likely also be explained by the fact that investors do not traditionally make big moves during weekends, as the traditional markets are closed during this time. A very important benchmark and investment tool are financial indices, which allow investors to obtain information on the current state of the market. Furthermore, indices that are turned into tradable assets and derivatives thereon improve market accessibility.
What is Volatility?
As the crypto market continues to evolve, embracing volatility will become increasingly important. Staying informed, adapting to market conditions, and maintaining a long-term perspective can help traders make the right decisions. Technical analysis is also a popular approach used by traders to predict and manage volatility in the cryptocurrency market. Various technical analysis tools can assist in identifying patterns, trends, and potential price movements. News, social media, and trader sentiment can heavily influence the demand and supply dynamics of cryptocurrencies, leading to volatile price movements.
This is a great position to be in as it is much more lucrative than simply providing liquidity for a small fee and being at risk of impermanent loss. Past that, volatility creates opportunities for traders looking to make a profit by buying and selling assets. The NR algorithm is used to compute the volatility surface for each timestamp in the sample. This leaves us, for every point in time t, with a surface of implied volatilities σ(τ,K) that spans over all strikes K and maturities τ of the available options. Nevertheless, it is important to make a significant distinction when talking about volatility among cryptocurrencies. Specifically, this distinction is to separate crypto volatility and Bitcoin volatility.
Strategies for Trading Crypto During Volatile Phases
However, the accuracy seems to fall off gradually as the sliding window size increases, the first sliding window sizes perform better than all other benchmarks with the exception of our method (AT-LSTM-MLP). SMA with sliding window size of 2 yields the best result of 2.02, 2.63, and 2.16 in MAE, RMSE, and SMAPE, respectively. Particularly, SMA only works well with stable data, i.e. when the difference between Non-deliverable Forward Ndf time stamps within a sliding time window is small. Nevertheless, volatility indexes like these are nearly useless without the underlying knowledge to understand them. Be sure to check out Ivan on Tech Academy, the best place to learn about blockchain, to get all the basic knowledge to become a trading pro. What’s more, our blog is updated with free, in-depth articles like this one on a daily basis.
The two resulting volatility indices are cointegrated and the corresponding error correction model can be utilized as a metric for market implied tail-risk. Many of the reasons for price volatility in mainstream markets hold true for cryptocurrencies as well. News developments and speculation are responsible for fueling price swings in crypto and mainstream markets alike.
Severity of price fluctuation
In other words, if it’s all crypto doom and gloom on TikTok and X, expect downward volatility swings. In the crypto space, users call this ‘buying the dip’ and ‘taking profit’ — in other words, as volatility accompanies the crypto market, one can wait for a price dip to buy and often sell on a high soon after. Understanding volatility is crucial for anyone involved in the cryptocurrency market. By grasping the concept of volatility, traders are able to make more informed decisions and mitigate potential risks.
Section 3.1 lays out general index rules, such as option selection criteria and the interpolation method. Those index rules are designed to be as similar to existing volatility indices as possible, while accounting for the specifics of cryptocurrency markets. Sections 3.2 and 3.3 introduce two alternative volatility measures that are suitable for the index.
Volatility and Liquidity in Cryptocurrency Markets—The Causality Approach
If we assume the cryptocurrency industry will continue to grow more diversified, crypto volatility could diverge further from Bitcoin volatility. As the name indicates, Bitcoin volatility technically refers to the price volatility of Bitcoin. On the other hand, crypto volatility can be seen as the overall volatility of the crypto market. It is easy to see how differentiating between the two will potentially become more important in the future. As Bitcoin’s industry dominance appears to be waning, this distinction potentially becomes even more significant.
Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings. Every element of the crypto sector is new and evolving daily, so it makes sense to approach cryptocurrencies with a degree of caution as well as excitement. If a trader expects that some large-scale shock can affect the whole market, he can buy CVI and, if the market downturn really happens, the trader can make substantial gains from the trade. Cryptocurrencies have revolutionised the financial landscape with their decentralised and digital nature. However, they also come with a characteristic that typically influences them more than most fiat currencies — volatility.
VIX (volatility index)
This is defined as a measure of the 30-day future fluctuation degree of the price of the entire cryptocurrency market using the Black-Scholes option pricing model. In this way, an index that fluctuates between 0 and 200 is developed, such that 200 will indicate the maximum level of implied volatility in the market whilst a value of zero indicates the lowest volatility [10]. This index is intended to prevent investors from putting themselves at risk by modifying their trading strategy in line with different values of CVI. The higher the CVI value is, the greater the risks are but also the greater the potential return is. Cryptocurrency option liquidity is centred on Bitcoin, which is currently a limit to the accessibility of cryptocurrency volatility.
LSTM comes into second place with errors at more than double comparing to AT-LSTM-MLP. Whereas, the three remaining methods show poor results as the predicted values are too far from real values. These results give an answer to our research question that AT-LSTM-MLP can predict the future value of the CVI index well. Simple methods based on statistical learning frameworks have been found to show good performance in many studies, e.g. Simple Moving Average (SMA) [2], Support Vector Regression (SVR) [11] and Random Forest (RF) [32].
View All Financial Services & Investing
On the other hand, the emergence of the derivative market has signaled the need for solid pricing strategies as well as reliable risk measures. There is a growing need for a new decentralized volatility index that provides a proper estimation of the risk measurement of the cryptocurrency components, and a delivery of market status information to potential investors. Government regulations or policy changes can affect how cryptocurrency can be used and is viewed, leading to increased volatility. A recent example is the approval of spot Bitcoin exchange-traded funds (ETFs) in the US, which led to billions of US dollar inflows into the funds and price volatility. The results from this study contribute to literature on the cryptocurrency market with some useful tools and information that aim to helping the investors in making decisions in investment. We believe that our method can be applied to other prediction tasks that involve time series because of its good performance.
This is, since cryptocurrency options were introduced in 2016, market liquidity and participation has improved significantly. According to data from Skew2, the total number of outstanding contracts (open interest) has more than tripled from its 2019 value, reaching a market size above USD 1 billion for the first time in mid-2020. This surge in size provides a great opportunity to tap a very interesting source of volatility information. Nevertheless, our volatility indexing method addresses remaining liquidity concerns for this young asset class, ultimately allowing us to extract stable cryptocurrency volatility information. According to data from SkewFootnote 2, the total number of outstanding contracts (open interest) has more than tripled from its 2019 value, reaching a market size above USD 1 billion for the first time in mid-2020. At its core, CVI tracks the 30-day implied volatility of major cryptocurrencies, namely Bitcoin and Ethereum.