This study examines the information flow between prices and transaction volumes in the cryptocurrency market, where transfer entropy is used for measurement. We selected four cryptocurrencies (Bitcoin, Ethereum, Litecoin and XRP) with large market values, and Bitcoin and BCH (Bitcoin Cash) for hard fork analysis; a hard fork is when a single cryptocurrency splits in two. By examining the real price data, we show that the long-term time series includes too much noise obscuring the local information flow; thus, a dynamic calculation is needed. The long-term and short-term sliding transfer entropy (TE) values and the corresponding [Formula: see text]-values, based on daily data, indicate that there is a dynamic information flow. The dominant direction of which is [Formula: see text]. In addition, the example based on minute Bitcoin data also shows a dynamic flow of information between price and transaction volume. The priceâvolume dynamics of multiple time scales helps to analyze the price mechanism in the cryptocurrency market.
We assess the qualification of Crypto Currency as a new emerging financial asset class using Bitcoin as a sample study. As a financial asset class, its value should be derived from business prospects, uncertainty, and opportunity cost of money (riskless rate). We model the asset value relationship in form of an error correction model in regard of possible nonstationary data properties. We use GSCI commodity index, S&P 500 Index, Economic Policy Uncertainty Index and Yield of 5-year US Treasury Bonds as proxies of explanatory variables. Our findings show that the notion of crypto currency as a financial asset might be spurious. Common stochastic trend is the source of apparent correlation between Bitcoin and the regressors. This lack of fundamental linkage opens a way to improve cryptocurrency business model for greater global acceptance.
Cryptocurrency market has a potential growth since Bitcoin emerged as one of the commodity investment in Indonesia. Besides Bitcoin, there is another altcoin which rapidly developing and dominating the cryptocurrency market, Ethereum. Therefore, this study aims to analyze the factors that affect Ethereum prices through macroeconomic aspects, such as EUR/USD exchange rate and the price of gold, as well as Bitcoin and other altcoins prices in the cryptocurrency market. The time series data consists of weekly data of all variables will be utilized during the 2016-2018 period. This study will conduct an empirical analysis by using the Autoregressive Distributed Lag (ARDL) test model. The result found that only in the short term, EUR/USD affects the Ethereum prices, while the price of gold do not show any effect on Ethereum prices. Moreover, Bitcoin and 2 alt-coins (Litecoin, and Monero) affect significantly the Ethereum prices. Nevertheless, Ripple and Stellar do not show significant effect on Ethereum prices.
This article empirically investigates some of the key features of cryptocurrency returns and volatilities, such as their relationship with traditional asset classes, as well as the main driving factors behind market activity. The main empirical results suggest that while there is a mild relationship between returns on cryptocurrencies and commodities, and precious metals in particular, the relationship does not translate into volatility spillover effects. Consistent with existing theoretical models in which trading activity is primarily driven by investor sentiment, we show that trading volume is driven by past returns. On the other hand, macroeconomic factors do not seem to affect market activity in either the short term or the long term. <b>TOPICS:</b>Currency, exchanges/markets/clearinghouses <b>Key Findings</b> ⢠There is only a mild, and not significant, correlation between returns on cryptocurrencies and returns on traditional asset classes on a daily basis. ⢠Past returns significantly drive trading volume, consistent with the idea that short-term market activity is primarily driven by sentiment. ⢠Macroeconomic factors such as the term structure of interest rates and inflation expectations do not seem to affect market activity in either the short or the long term.
The main purpose of our paper is to evaluate the impact of the COVID-19 pandemic on randomness in volatility series of world major markets and to examine its effect on their interconnections. The data set includes equity (Bitcoin and Standard and Poorâs 500), precious metals (Gold and Silver), and energy markets (West Texas Instruments, Brent, and Gas). The generalized autoregressive conditional heteroskedasticity model is applied to the return series. The wavelet packet Shannon entropy is calculated from the estimated volatility series to assess randomness. Hierarchical clustering is employed to examine interconnections between volatilities. We found that (i) randomness in volatility of the S&P500 and in the volatility of precious metals were the most affected by the COVID-19 pandemic, while (ii) randomness in energy markets was less affected by the pandemic than equity and precious metal markets. Additionally, (iii) we showed an apparent emergence of three volatility clusters: precious metals (Gold and Silver), energy (Brent and Gas), and Bitcoin and WTI, and (iv) the S&P500 volatility represents a unique cluster, while (v) the S&P500 market volatility was not connected to the volatility of Bitcoin, energy, and precious metal markets before the pandemic. Moreover, (vi) the S&P500 market volatility became connected to volatility in energy markets and volatility in Bitcoin during the pandemic, and (vii) the volatility in precious metals is less connected to volatility in energy markets and to volatility in Bitcoin market during the pandemic. It is concluded that (i) investors may diversify their portfolios across single constituents of clusters, (ii) investing in energy markets during the pandemic period is appealing because of lower randomness in their respective volatilities, and that (iii) constructing a diversified portfolio would not be challenging as clustering structures are fairly stable across periods.
This research has examined the ability of two forecasting methods to forecast Bitcoin's price trends. The research is based on Bitcoin-USA dollar prices from the beginning of 2012 until the end of March 2020. Such a long period of time that includes volatile periods with strong up and downtrends introduces challenges to any forecasting system. We use particle swarm optimization to find the best forecasting combinations of setups. Results show that Bitcoin's price changes do not follow the "Random Walk" efficient market hypothesis and that both Darvas Box and Linear Regression techniques can help traders to predict the bitcoin's price trends. We also find that both methodologies work better predicting an uptrend than a downtrend. The best setup for the Darvas Box strategy is six days of formation. A Darvas box uptrend signal was found efficient predicting four sequential daily returns while a downtrend signal faded after two days on average. The best setup for the Linear Regression model is 42 days with 1 standard deviation.
Prosper Lamothe-FernĂĄndez, David Alaminos, Prosper Lamothe-LĂłpez, Manuel Ă. FernĂĄndez-GĂĄmez
A precise prediction of Bitcoin price is an important aspect of digital financial markets because it improves the valuation of an asset belonging to a decentralized control market. Numerous studies have studied the accuracy of models from a set of factors. Hence, previous literature shows how models for the prediction of Bitcoin suffer from poor performance capacity and, therefore, more progress is needed on predictive models, and they do not select the most significant variables. This paper presents a comparison of deep learning methodologies for forecasting Bitcoin price and, therefore, a new prediction model with the ability to estimate accurately. A sample of 29 initial factors was used, which has made possible the application of explanatory factors of different aspects related to the formation of the price of Bitcoin. To the sample under study, different methods have been applied to achieve a robust model, namely, deep recurrent convolutional neural networks, which have shown the importance of transaction costs and difficulty in Bitcoin price, among others. Our results have a great potential impact on the adequacy of asset pricing against the uncertainties derived from digital currencies, providing tools that help to achieve stability in cryptocurrency markets. Our models offer high and stable success results for a future prediction horizon, something useful for asset valuation of cryptocurrencies like Bitcoin.
Taking the unique advantage of the cryptocurrency market setting, this paper examines the relationships between blockchain participation and returns, trading volume and realized volatility of main cryptocurrencies (i.e., Bitcoin, Ethereum and Litecoin). Dissimilar to previous theoretical studies that model the influencing factors on participation, we employ the number of unique from addresses 1 as the proxy for cryptocurrency investorsâ blockchain participation and further explore the impact of such participation. By using vector autoregressive (VAR) model, we find that the blockchain participation has a significant and positive impact on the next dayâs trading volume and realized volatility for the main cryptocurrencies. Our results are robust to the Granger causality test and alternative measure for blockchain participation.
Chun Kwong Koo, Artur Semeyutin, Chi Keung Marco Lau, Jian Fu
We study the tailsâ behavior of four major Cryptocurrencies (Bitcoin, Litecoin, Ethereum and Ripple) by employing the Autoregressive Fr´echet model for conditional maxima. Using five-minute-high-frequency data, we report time-evolving tails as well as provide a straightforward measure of tails asymmetry for positive and negative intra-day returns. We find that only Bitcoin has a notable more massive tail for positive returns asymmetry while the remaining three Cryptocurrencies have a general tendency towards more massive negative intra-day tails. All considered Cryptocurrencies depict lighter tails as the market matures.
This paper studies the impact of fear sentiment caused by the coronavirus pandemic on Bitcoin price dynamics. We construct a new proxy for coronavirus fear sentiment using hourly Google search queries on coronavirus-related words. The results show that market volatility has been exacerbated by fear sentiment as the result of an increase in search interest in coronavirus. Moreover, we find that negative Bitcoin returns and high trading volume can be explained by fear sentiment regarding the coronavirus. Our results also show that Bitcoin fails to act as a safe haven during the pandemic.
Venelina Nikolova, Juan Evangelista Trinidad Segovia, M. FernĂĄndezâMartĂnez, M.A. SĂĄnchez-Granero
One of the main characteristics of cryptocurrencies is the high volatility of their exchange rates. In a previous work, the authors found that a process with volatility clusters displays a volatility series with a high Hurst exponent. In this paper, we provide a novel methodology to calculate the probability of volatility clusters with a special emphasis on cryptocurrencies. With this aim, we calculate the Hurst exponent of a volatility series by means of the FD4 approach. An explicit criterion to computationally determine whether there exist volatility clusters of a fixed size is described. We found that the probabilities of volatility clusters of an index (S&P500) and a stock (Apple) showed a similar profile, whereas the probability of volatility clusters of a forex pair (Euro/USD) became quite lower. On the other hand, a similar profile appeared for Bitcoin/USD, Ethereum/USD, and Ripple/USD cryptocurrencies, with the probabilities of volatility clusters of all such cryptocurrencies being much greater than the ones of the three traditional assets. Our results suggest that the volatility in cryptocurrencies changes faster than in traditional assets, and much faster than in forex pairs.
This paper examines the risk connectedness across the seven cryptocurrencies, Bitcoin, Ethereum, Ripple, Litecoin, Stellar, Monero and Dash, who admit large capitalizations in the cryptocurrency market. The data sample is from August 7, 2015 to February 15, 2020. We apply the CAViaR model to measure the return risks of the cryptocurrencies, showing their similar risk tendencies with volatility clusterings during the beginning of 2017 and the end of 2018. The net pairwise spillover index developed by Diebold and Yilmaz (2012) is use as the measure for the risk connectedness among the cryptocurrencies. We find that the risk spillover directions are highly correlative with the capitalizations of the cryptocurrencies. The cryptocurrencies with small capitalizations transmit risks to those with large cryptocurrencies. In the risk downward tendency, the risk spillover levels among the cryptocurrencies are stronger than that in the risk upward tendency, while the spillover directions keep the same in both risk tendencies, except the cryptocurrency Monero, which may be due to the trading volume difference from the others. We use the generalized forecast error variance decomposition for the spillover index and explore the risk connectedness across the cryptocurrencies in differen time frequencies, including the short term (0-4 days), medium term (4-30 days) and long term (30-300 days) frequency. The risk spillovers in the short term frequency can be neglected, which implies the delay effects of risk spillovers. The risk spillovers in medium term frequency are mostly stronger than that in long term frequency. The dynamic connectedness result shows the risk spillover mean in the long term frequency is larger than that in the medium term frequency. An inverse result holds for the risk spillover range. The risk spillover fluctuations in the long and medium term frequency admit the coincident comparison for spillover levels in these two frequencies. The findings in this paper provide suggestions for regulators controlling the market stability and investors generating investment strategies.
We examine diversification when cryptocurrencies are included in investment portfolios, around China prohibiting initial coin offerings on 4 September 2017. We discover, once we account for liquidity, that all portfolio diversification benefits of cryptocurrencies are eliminated.
An important aspect of liquidity is price risk, i.e., the risk that a small transaction leads to a large price change. This usually happens in a thin market, when trading opportunities are scarce and the time between subsequent trades is long. We rely on an autoregressive conditional duration model to extract the probability of a substantial price event in a particular time interval and, thus, an intraday risk profile. Our findings show that price risk is highest at times when European and U.S. investors do not trade. In a second step, we relate daily aggregates to characteristics of the Bitcoin blockchain and investigate whether investors account for features like confirmation time or fees when timing their orders.
Bitcoin has attracted extensive attention from investors, researchers, regulators, and the media. A well-known and unusual feature is that Bitcoinâs price often fluctuates significantly, which has however received less attention. In this paper, we investigate the Bitcoin price fluctuation prediction problem, which can be described as whether Bitcoin price keeps or reversals after a large fluctuation. In this paper, three kinds of features are presented for the price fluctuation prediction, including basic features, traditional technical trading indicators, and features generated by a Denoising autoencoder. We evaluate these features using an Attentive LSTM network and an Embedding Network (ALEN). In particular, an attentive LSTM network can capture the time dependency representation of Bitcoin price and an embedding network can capture the hidden representations from related cryptocurrencies. Experimental results demonstrate that ALEN achieves superior state-of-the-art performance among all baselines. Furthermore, we investigate the impact of parameters on the Bitcoin price fluctuation prediction problem, which can be further used in a real trading environment by investors.
Erdinç AkyĹldĹrĹm, Shaen Corbet, Douglas J. Cumming, Brian M. Lucey ¡ 5 authors
Cryptocurrencies have been broadly scrutinised in recent times for a host of concerning regulatory and cybercriminality issues. Although steps have been taken to promote regulatory sufficiency in the near future, we examine the avenues through which this extremely high-risk industry can derive potentially devastating contagion channels, influencing both unwilling and unsuspecting investors. We focus this research on the expressions of interest by publicly traded companies across the world to utilise cryptocurrency and blockchain projects. We find evidence that there exists a substantial stock price premium and sustained increase in volatility in the aftermath of blockchain announcements, with emphasis on highly-speculative motives such as coin creation and corporate name changes. Changes in price discovery and information flows are found to be largely determined from cryptocurrency-based pricing sources in the aftermath of speculative announcements. We discuss the inherent ethical and legal issues, considering as to whether such announcements are simply an attempt to artificially manipulate share prices and take part in the current phase of crypto-exuberance.
The emerging interest in Bitcoin futures market has led to questions on its trading form and contribution to risk minimization. These questions are important for market participants, including hedgers and speculators. This paper addresses the possible trading motive in Bitcoin futures market in being speculation or hedging. The author first tests a model relating Bitcoin futures returns with trading volume and conditional volatility, estimated with a GJR-GARCH specification, on a full sample of daily futures prices. A robustness check is then conducted by investigating the hedging effectiveness of Bitcoin futures and the speculation-hedging ratios on individual Bitcoin futures contracts. The estimation results on Bitcoin futures contracts, spanning from December 2017 to February 2020, show a significant positive relationship between futures returns and lagged volume. The speculation-hedging measures used for Bitcoin futures contracts maturing in March, June, September, and December reveal an increasing demand for speculation. Also, the Bitcoin spotâs full-hedge and OLS-hedge strategies with Bitcoin futures provide no gain over a no-hedge strategy. The results reveal strong evidence that traders in the Bitcoin futures market are motivated by speculation rather than hedging. This further puts in evidence the existence of asymmetric information within informed traders in Bitcoin futures market, and therefore market participants would not insure their positions against Bitcoin price movements.