Through a data-driven analysis, namely network analysis, we investigate the relationships between all existing cryptocurrencies. Starting from the analysis of cryptocurrencies in 2013, we extend our study until July 2018 to study the interdependencies between 1636 cryptocurrencies. Our study shows that, although Bitcoin is the older and the most famous cryptocurrency, it does not appear as an influential asset on the virtual currency market. Our analysis also indicates a densification of the interconnections between virtual currencies, indicating that change of a single coin will likely influence many other coins. Interestingly, we also observe that the most influential cryptocurrencies for a year appear not to be influential the following year. Finally, cryptocurrencies tend to change their influence over time suggesting a short-term interdependence between them. JEL: G11, G12
While relevant stylized facts are observed for Bitcoin markets, we find a distinct property for the scaling behavior of the cumulative return distribution. For various assets, the tail index $Ό$ of the cumulative return distribution exhibits $Ό\approx 3$, which is referred to as "the inverse cubic law." On the other hand, that of the Bitcoin return is claimed to be $Ό\approx 2$, which is known as "the inverse square law." We investigate the scaling properties using recent Bitcoin data and find that the tail index changes to $Ό\approx 3$, which is consistent with the inverse cubic law. This suggests that some properties of the Bitcoin market could vary over time. We also investigate the autocorrelation of absolute returns and find that it is described by a power-law with two scaling exponents. By analyzing the absolute returns standardized by the realized volatility, we verify that the Bitcoin return time series is consistent with normal random variables with time-varying volatility.
Abstract Many corporate executives believe blockchain technology is broadly scalable and will achieve mainstream adoption, yet there is little evidence of significant shareholder value creation associated with corporate adoption of blockchain technology. We collect a broad sample of firms that invest in blockchain technology and examine the stock price reaction to the âfirstâ public revelation of this news. Initial reactions average close to +13% and are followed by reversals over the next 3 months. However, we report a striking difference based on the credibility of the investment. Blockchain investments that are at an advanced stage or are confirmed in subsequent financial statements are associated with higher initial reactions and little or no reversal. The results suggest that credible corporate strategies involving blockchain technology are viewed favorably by investors.
The circumstances surrounding the outbreak of the COVID-19 pandemic have generated substantial international political strain as governments attempt to mitigate the widespread associated social and economic repercussions. One theory has focused on the potential for Chinese informational asymmetry. Using Chinese financial market data, we attempt to establish the scale and direction of information flows during multiple distinct phases of the development of the pandemic. Two specific results are identified. Firstly, the majority of domestically-traded Chinese stocks present evidence of significant information flows at a far earlier stage than internationally-traded comparatives, suggesting that domestic investors recognised the dangers associated with COVID-19 far in advance of the rest of the world. One potential explanation surrounds the view that the severity of domestically-reported Chinese news was not appropriately recognised by international investors. Secondly, while evidence of safe-haven and flight-to-safety behaviour is evident throughout traditional energy and precious metal markets, cryptocurrencies became informationally-synchronised with Chinese equity markets, indicating their use as an investor safe-haven. This is a particularly concerning outcome for international policy-maker and regulatory authorities due to the fragility of these developing markets.
Shaen Corbet, Paraskevi Katsiampa, Chi Keung Marco Lau
This paper studies causal relationships and the potential of improving conditional quantile forecasting between Bitcoin and seven altcoin markets as well as between Bitcoin and three mainstream assets, namely gold, oil, and the S&P500, by applying the Granger-causality in distribution and in quantiles tests. We find significant bidirectional causality between Bitcoin and all altcoins and assets considered in the two distribution tails. An enhanced forecast of Bitcoin price returns is thus derived by conditioning on altcoins or assets and vice versa during extreme market conditions. However, under normal market conditions the results for the centre of the distribution of the Bitcoin price returns conditional on altcoins depend on both the altcoin considered and quantile under investigation. We also find evidence that Bitcoin is not isolated from financial markets, while this developing financial asset is a strong safe-haven for oil and a weak safe-haven for S&P500, but it cannot be considered as either a weak or strong safe-haven for gold. Our results reveal a more complete relationship between Bitcoin and altcoins as well as financial assets than was previously considered.
This study explores the connectedness between cryptocurrencies (Bitcoin, Ethereum, Ripple, Bitcoin cash and Ethereum Operating System) and major stock markets (NYSE composite index, NASDAQ composite index, Shanghai Stock Exchange, Nikkei 225 and Euronext NV). Using the asymmetric dynamic conditional correlation (ADCC) and wavelet coherence approaches, we document a significant time-varying conditional correlation between the majority of the cryptocurrencies and stock market indices and that the negative shocks play a more prominent role than the positive shocks of the same magnitude. Overall, our findings explore potential avenues for diversification for investors across cryptocurrencies and major stock markets.
Abstract Being more and more popular in the past 10 years, Bitcoin has drawn extensive attention from the press, scholars, and practitioners. The aim of this paper is to investigate which predictor is more predictive for Bitcoin volatility from the aspects of inâsample and outâofâsample in a highâspeed changing world. We utilise the GARCHâMIDAS model to examine the predictive power of five crucial predictors, including VIX, GVZ, Google Trends, GEPU, and GPR. Our findings provide strong evidence that GVZ exhibits strongest predictability for Bitcoin volatility over other competing predictors. Other empirical results based on different outâofâsample forecasting periods, alternative loss functions and combination methods further ensure our major conclusions are robust.
This study employed an augmented AR-GJRGARCH model that incorporates an intraday-based buy-sell order size imbalance measure to explore how informed trading behaviour/activity impacted Bitcoin returns and volatility from January 2014 to February 2019. Our results show that the informed trading behaviour dominated by sell-order significantly led to decreased concurrent Bitcoin returns for alternative sample periods. However, the informed trading behaviour dominated by buy-order related positively to Bitcoin returns only for the full sample period. Moreover, the informed trading activity helped to reduce Bitcoinâs volatility, which is consistent with expectations based on dispersion of beliefs models. Finally, we uncovered a positive (inverted) asymmetric volatility effect for both the full and the rising sample periods, indicating the presence of the âfear of missing outâ psychological effect.
The article explores the correlation strength of the ten most important cryptocurrencies, emphasizing the examination of differences during the periods of rising and falling prices. The daily and weekly returns of selected cryptocurrencies are taken as the basis for calculating and determining the correlation strength using the Pearson correlation coefficient. The survey covers the period from the beginning of 2017 to Bitcoinâs last local bottom in mid-March 2020. Research findings are as follows: 1) the most important cryptocurrencies are mostly moderately positively correlated with each other over time; 2) correlation strength decreases slightly during the bull period, but mostly remain in the range of moderate correlation; 3) correlation strength increases significantly during the bear period, with most cryptocurrencies strongly correlated with each other. The results do not change significantly if the daily or weekly cryptocurrency returns are used as the basis. A strong correlation in the period of falling prices prevents the effective diversification of the cryptocurrency portfolio, which must be considered when investing funds in the cryptocurrency market.
Shaen Corbet, Yang Hou, Yang Hu, Les Oxley · 5 authors
Utilising Chinese-developed data based on long-standing influenza indices, and the more recently-developed coronavirus and face mask indices, we set out to test for the presence of volatility spillovers from Chinese financial markets upon a broad number of traditional financial assets during the outbreak of the COVID-19 pandemic. Such indices are used to specifically measure the performance of Chinese companies who are inherently involved in the R&D and production of materials and products used to mitigate and counteract the effects of influenza and coronavirus, therefore, such indices present a unique barometer of broad population-based sentiment relating to COVID-19 in comparison to traditional Chinese influenza. Within days of the formal announcement of the COVID-19 outbreak, results indicate exceptionally pronounced and persistent impacts of the coronavirus pandemic upon Chinese financial markets, compared to that of the traditional and long-standing influenza index. Further, in a novel finding to date, COVID-19 is found to have had a substantial effect on directional spillovers upon the Bitcoin market. Cryptocurrency-based confidence appears to have been instigated through government-developed education schemes, which are identified as one possible explanation for our results, which are found to remain robust across both data-frequency and methodological variation.
With the development of energy interconnection and increasing consumption of natural gas, the distributed energy supply and trusted-transaction- based natural gas Industrial Internet of Things (IIoT) has become one of the hottest research spots in the energy industry. This article puts forward natural gas IIoT architecture based on blockchain and AI, in order to address the defects of centralized energy supply architecture. The proposed architecture is introduced in detail from three aspects of infrastructure, side-chain of natural gas block based on data dimension, and backbone of natural gas block based on value dimension. Then the design issues on the integration existence of blockchain and AI in an actual natural gas IIoT scenario are discussed, including data association and interaction of blockchains, trusted identity authentication and management, energy source and virtual currency transformation, and so on. Next, an LSTM-based natural gas load prediction model is proposed by virtue of AI technology. The transaction model based on natural gas value and supply-demand relationship is also proposed by choosing the common natural gas application scenarios and taking advantage of blockchain technology. Experiments show that the proposed models can predict demand and output load of natural gas, while achieving the balance of interests of natural gas suppliers, users, and market in the transaction scenarios.
This paper investigates volatility spillovers among six competitor Cryptocurrencies from August 8, 2015 to September 01, 2019. A Generalized VAR framework is used to measure time varying spillovers index. Results provide evidence of (i) a rise in volatility spillovers transmitted among monitored Cryptocurrencies since the second quarter of 2017. (ii) Ethereum acts as the major contributor on spillovers index, contrary to Ripple that presents the main recipient of spillovers. (iii) the pairwise (Monero-Ripple) and (Bitcoin-Ethereum) present a low connectedness level driving consequently beneficial diversification opportunities for cryptocurrency investors.
Abstract This study investigates how twelve cryptocurrencies with large capitalization get influenced by the three cryptocurrencies with the largest market capitalization (Bitcoin, Ethereum, and Ripple). Twenty alternative specifications of ARCH, GARCH as well as DCC-GARCH are employed. Daily data covers the period from 1 January 1 2018 to 16 September 2018, representing the intense bearish cryptocurrency market. Empirical outcomes reveal that volatility among digital currencies is not best described by the same specification but varies according to the currency. It is evident that most cryptocurrencies have a positive relationship with Bitcoin, Ethereum and Ripple, therefore, there is no great possibility of hedging for crypto-currency portfolio managers and investors in distressed times.
Jakub JeÄmĂnek, Gabriela KukalovĂĄ, LukĂĄĆĄ Moravec
Abstract Since Bitcoin introduction in 2008, the cryptocurrency market has grown into hundreds-of-billion-dollar market. The cryptocurrency market is well known as very volatile, mainly for the fact that the cryptocurrencies have not the price to fall back upon and that anybody can join the trading (no license or approval is required). Since empirical literature suggests that GARCH-type models dominate as VaR estimators the overall objective of this paper is to perform comprehensive volatility and VaR estimation for three major digital assets and conclude which method gives the best results in terms of risk management. The methods we used are parametric (GARCH and EWMA model), non-parametric (historical VaR) and Monte Carlo simulation (given by Geometric Brownian Motion). We conclude that the best method for value-at-risk estimation for cryptocurrencies is the Monte Carlo simulation due to the heavy diffusion (stochastic) process and robustness of the results.
The present paper aims to examine the relationship between price movement of cryptocurrency (Bitcoin) and stock exchange movements of two major global economies i.e. India and China. 1133 number of observations on daily basis were taken from 1st January 2015 to 29th November 2019 and analysed using statistical software E-views. Statistical techniques like Granger Causality, Johnsen Co-integration and VECM have been employed to achieve the objective of the paper. The empirical results of the paper depict that long run relationship exists between Bitcoin and stock exchanges of India and China. Sensex has the unidirectional causality with Bitcoin. The significant t-statistics imply an influential role of Sensex in Bitcoin price movement. The results further indicate that there is no evidence of any causal relationship between Bitcoin and Chinese Stock exchange, which suggests a better risk-return mechanism for the global investors and policy makers. The findings of the paper can be imparted as guidelines for the global investors for diversifying their portfolios.
Ahmed H. Elsayed, Giray Gözgör, Chi Keung Marco Lau
Abstract This paper utilizes two methods to uncover the causality dynamic between the three leading cryptocurrencies: Bitcoin, Litecoin, Ripple, and nine major foreign currency markets. Firstly, we implement the technique of DieboldâYilmaz to compute the spillover index between cryptocurrencies and currency markets. We find a significant return spillover effect between Bitcoin and Litecoin in the first three quarters of 2017. Still, the return spillover is merely meaningful in the first three quarters of 2015 for Ripple. However, the total volatility spillover index in the system decreases in the fourth quarter of 2017. Secondly, we apply the Bayesian graphical structural vector, autoregressive estimations, and find that the current level of Bitcoin depends only on the previous level of the Chinese Yuan. The current level of Ripple strongly depends on the prior levels of Bitcoin, followed by Litecoin. The current level of Litecoin strongly depends on the previous level of Ripple, followed by the Chinese Yuan. These results indicate that there is a significant causal relationship among cryptocurrencies. However, except for the Chinese Yuan, major traditional currencies do not significantly affect cryptocurrencies.