Rubaiyat Islam, Yoshi Fujiwara, Shinya Kawata, Hiwon Yoon
In this study, we analyzed bitcoin blockchain data for the period between 2013 and 2018. We constructed daily networks and analyzed the network properties of the bitcoin users and bitcoin flow attributed as edge flow circulated between users to focus on weekdays and weekends activities. In the real world, businesses take time off, particularly on weekends. This is no different in the crypto-asset world, which, theoretically, is operable 24/7. We also performed a threshold analysis of the flow of bitcoin to identify the big wallets and to compare it with the identity of real-world crypto-exchange companies, in particular, their weekly patterns. Finally, we propose a methodology to identify the financial institution in the bitcoin blockchain on the basis of fulfilling some key criteria. The criteria are having high frequency, appearing persistently on daily big trades and showing a distinct weekly pattern of total average network flow.
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.
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 spot’s 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.
Raja Nabeel‐Ud‐Din Jalal, Massimo SARGIACOMO, Najam Us Sahar, Um‐E‐Roman Fayyaz
The study investigates herding behavior in cryptocurrencies in different situations. This study employs daily returns of major cryptocurrencies listed in CCI30 index and sub-major cryptocurrencies and major stock returns listed in Dow-Jones Industrial Average Index, from 2015 to 2018. Quantile regression method is employed to test the herding effect in market asymmetries, inter-dependency and intra-dependency cases. Findings confirm the presence of herding in cryptocurrency in upper quantiles in bullish and high volatility periods because of overexcitement among investors, which lead to high volume trading. Major cryptocurrencies cause herding in sub-major cryptocurrencies, but it is a unidirectional relation. However, no intra-dependency effect among cryptocurrencies and equity market is observed. Results indicate that in the CKK model herding exists at upper quantile in market that may be due when the market is moving fast, continuously trading, and bullish trend are prevailing. Further analysis confirms this narrative as, at upper quantile, the beta of bullish regime is negative and significant, meaning the main source of market herding is a bullish trend in investment, which increases market turbulence and gives investors opportunity to herd. Also, we found that herding in cryptocurrencies exits in high volatility periods, but this herding mostly depends on market activity, not market movement.
We investigate the effects of the recent financial turbulence of 2020 on the market of cryptocurrencies taking into account the hourly price and volume of transactions from December 2019 to April 2020. The data were subdivided into time frames and analyzed the directed network generated by the estimation of the multivariate transfer entropy. The approach followed here is based on a greedy algorithm and multiple hypothesis testing. Then, we explored the clustering coefficient and the degree distributions of nodes for each subperiod. It is found the clustering coefficient increases dramatically in March and coincides with the most severe fall of the recent worldwide stock markets crash. Further, the log-likelihood in all cases bent over a power law distribution, with a higher estimated power during the period of major financial contraction. Our results suggest the financial turbulence induce a higher flow of information on the cryptocurrency market in the sense of a higher clustering coefficient and complexity of the network. Hence, the complex properties of the multivariate transfer entropy network may provide early warning signals of increasing systematic risk in turbulence times of the cryptocurrency markets.
Huthaifa Alqaralleh, Ala’a Adden Abuhommous, Ahmad Alsaraireh
This study is set out to model and forecast the cryptocurrency market by concentrating on several stylized features of cryptocurrencies. The results of this study assert the presence of an inherently nonlinear mean-reverting process, leading to the presence of asymmetry in the considered return series. Consequently, nonlinear GARCH-type models taking into account distributions of innovations that capture skewness, kurtosis and heavy tails constitute excellent tools for modelling returns in cryptocurrencies. Finally, it is found that, given the high volatility dynamics present in all cryptocurrencies, correct forecasting could help investors to assess the unique risk-return characteristics of a cryptocurrency, thus helping them to allocate their capital.
Ismail O. Fasanya, Oluwatomisin J. Oyewole, Temitope F. Odudu
Purpose This paper examines the return and volatility spillovers among major cryptocurrency using daily data from 10/08/2015 to 15/04/2018. Design/methodology/approach The authors employ the Dielbold and Yilmaz (2012) spillover approach and rolling sample analysis to capture the inherent secular and cyclical movements in the cryptocurrency market. Findings The authors show that there is substantial difference between the behaviour of the cryptocurrency portfolios return and volatility spillover indices over time. The authors find evidence of interdependence among cryptocurrency portfolios given the spillover indices. While the return spillover index reveals increased integration among the currency portfolios, the volatility spillover index experiences significant bursts during major market crises. Interestingly, return and volatility spillovers exhibit both trends and bursts respectively. Originality/value This study makes a methodological contribution by adopting Dielbold and Yilmaz (2012) approach to quantify the returns and volatility transmissions among cryptocurrencies. To the best of our knowledge, little or no study has adopted the Dielbold and Yilmaz (2012) methodology to investigate this dynamic relationship in the cryptocurrencies market. The Dielbold and Yilmaz (2012) approach provides a simple and intuitive measure of interdependence of asset returns and volatilities by exploiting the generalized vector autoregressive framework, which produces variance decompositions that are unaffected by ordering.
Abstract Cryptocurrencies are unique and extra-ordinary currencies which to be econometrically forced into the linear model due to their systematic complexity and extreme movements. This paper was conducted to provide an alternative analysis as a solution for escaping the restrictions of traditional linear assumptions. Five predominant digital currencies such as Bitcoin (BTC), Stellar network (XLM), Litecoin (LTC), Ethereum Classic (ETC), and IOTA were chosen to be employed in the multiple processes based on Bayesian approaches. Market dominance and data regime classifications are the essential components that lead to successfully investigate the dependent structures and co-movements in the digital financial market. The empirical findings could assume that the modern time-series data was meticulously estimated by the flexible modern tool. Bayesian statistics and simulations have the sufficient potency as the suitable solution.
The work here presented moves from the acknowledgment that, even if blockchain technology has been around for more than ten years, the knowledge about its economic and business implications is fragmented and heterogeneous. In the first place, it is analyzed the shift from economics to tokenomics and the central role of the token within blockchain-based ecosystems. Subsequently, a generalized definition of the token is proposed. Diving into the requirements for a comprehensive description of tokens, that takes into account their wide variety, a comparative assessment of token classification frameworks available in the literature is performed. This analysis is leveraged to propose a new and comprehensive token classification framework, based on a morphological analysis representation. The proposed framework will be further refined with an empirical and iterative approach in future works.
Muhammad Yasir, Muhammad Attique, Khalid Latif, Ghulam Mujtaba Chaudhary · 7 authors
Purpose Business Intelligence has gained a significant attraction in the recent past and facilitates managers for efficient business decision-making. Over the years, the attraction toward the cryptocurrency (CC) market has increased. Since the CC market is highly volatile, it is extremely sensitive to shocks and web data related to large events happening around the globe. Design/methodology/approach This research study provides a business intelligence model to predict five top-performing CCs. In this study, deep learning, linear regression and support vector regression (SVR) are used to predict CC prices. The sentiment of some mega-events is also used to enhance the performance of these models. Findings The results show that models of business intelligence such as deep learning and SVR provide better results. Moreover, the results show that the incorporation of social media sentiment data significantly improves the performance of the proposed models. The overall accuracy of the model improves approximately twofold when multiple event sentiments were incorporated. Originality/value The use of social media sentiment of global and local events for different countries along with deep learning for CC forecasting.
Realized multipower variation, originally introduced to eliminate jumps, can be extremely useful for inference in pure-jump models. This article shows how to build a simple and precise estimator of the jump activity index of a semimartingale observed at a high frequency by comparing different multipowers. The novel methodology allows to infer whether a discretely observed process contains a continuous martingale component. The empirical part of the article undertakes a nonparametric analysis of the jump activity of bitcoin and shows that bitcoin is a pure jump process with high jump activity, which is critically different from conventional currencies that include a Brownian motion component.
Ziyang Ji, Victor Chang, Hao Lan, Ching-Hsien Robert Hsu · 5 authors
As one of the most significant components of financial technology (FinTech), blockchain technology arouses the interests of numerous investors in China, and the number of companies engaged in this field rises rapidly. The emotion of investors has an effect on stock returns, which is a hot topic in behavioral finance. Blockchain is an essential part of FinTech, and with the fast development of this technology, investors’ sentiment varies as well. The online information that directly reflects investors’ mood could be utilized for mining and quantifying to construct a sentiment index. For a better understanding of how well some factors adequately explain the return of stocks related to blockchain companies in the Chinese stock market, the Fama-French three-factor model (FFTFM) will be introduced in this paper. Furthermore, sentiment could be a new independent variable to enhance the explanatory power of the FFTFM. A comparison between those two models reveals that the sentiment factor could raise the explanatory power. The results also indicate that the Chinses blockchain industry does not own the size effect and book-to-market effect.
Analysing a set of 200 cryptocurrencies over the period from 2015 to 2019, we document a significant return reversal effect that holds at the daily, weekly, and monthly rebalancing frequencies and is robust to controls for differences in size, turnover, and illiquidity. Moreover, the reversal effect persists during both halves of our sample period and following periods of both high and low market implied volatility. Consistent with the effect being driven by a combination of market inefficiency and compensation for liquidity provision, we find reversals are most pronounced among smaller capitalization and less liquid cryptocurrencies.