Although cryptocurrencies are catching the fancy of investors for various benefits such as decentralization, low transaction costs, and inflation hedging, their extreme volatility is sometimes keeping many away. Consequently, modeling and forecasting cryptocurrency market volatility are essential to investors’ investment decisions and risk management. However, most previous studies have been limited to Bitcoin volatility, disregarding cryptocurrency market performance as a whole. This study estimates realized volatility of cryptocurrency market with a variety of algorithms employing a portfolio-style technique. After comparison, LSTM networks surpass the conventional GARCH-type models; meanwhile, the hybrid GARCH neural network models perform the worst. This study provides an impetus for a significant number of academics interested in the extreme volatility of cryptocurrencies. Additionally, it illustrates that more sophisticated models may not always lead to better predictive performance.
Maaz Khan, Maaz Khan, Umar Nawaz Kayani, Mrestyal Khan · 7 authors
Across the globe, COVID-19 has disrupted the financial markets, making them more volatile. Thus, this paper examines the market volatility and asymmetric behavior of Bitcoin, EUR, S&P 500 index, Gold, Crude Oil, and Sugar during the COVID-19 pandemic. We applied the GARCH (1, 1), GJR-GARCH (1, 1), and EGARCH (1, 1) econometric models on the daily time series returns data ranging from 27 November 2018 to 15 June 2021. The empirical findings show a high level of volatility persistence in all the financial markets during the COVID-19 pandemic. Moreover, the Crude Oil and S&P 500 index shows significant positive asymmetric behavior during the pandemic. Apart from this, the results also reveal that EGARCH is the most appropriate model to capture the volatilities of the financial markets before the COVID-19 pandemic, whereas during the COVID-19 period and for the whole period, each GARCH family evenly models the volatile behavior of the six financial markets. This study provides financial investors and policymakers with useful insight into adopting effective strategies for constructing portfolios during crises in the future.
This paper explores the asymmetric effect of COVID-19 pandemic news, as measured by the coronavirus indices (Panic, Hype, Fake News, Sentiment, Infodemic, and Media Coverage), on the cryptocurrency market. Using daily data from January 2020 to September 2021 and the exponential generalized autoregressive conditional heteroskedasticity model, the results revealed that both adverse and optimistic news had the same effect on Bitcoin returns, indicating fear of missing out behavior does not prevail. Furthermore, when the nonlinear autoregressive distributed lag model is estimated, both positive and negative shocks in pandemic indices promote Bitcoin's daily changes; thus, Bitcoin is resistant to the SARS-CoV-2 pandemic crisis and may serve as a hedge during market turmoil. The analysis of frequency domain causality supports a unidirectional causality running from the Coronavirus Fake News Index and Sentiment Index to Bitcoin returns, whereas daily fluctuations in the Bitcoin price Granger affect the Coronavirus Panic Index and the Hype Index. These findings may have significant policy implications for investors and governments because they highlight the importance of news during turbulent times. The empirical results indicate that pandemic news could significantly influence Bitcoin's price.
The uniqueness of this investigation lies in empirically testing and proving the contagion spillover of Bitcoin attention to carbon futures. Specifically, several models are adopted to investigate the explanatory and predictive abilities of Bitcoin attention to carbon futures. The results can be generalized as follows. First, Bitcoin attention Granger causes the variation of carbon futures. Second, Bitcoin attention shows a negative impact on carbon futures and an addition, an invert U-shaped connection exists. Third, the Bitcoin attention-based models can beat the commonly used historical average benchmark during out-of-sample forecasting both in statistical and economic levels. Fourth, we complete robustness checks to certify that the contagion spillover from Bitcoin attention to the pricing of carbon futures does exist. Finally, we prove the linear and non-linear impacts from Bitcoin attention to realized volatility of carbon futures. All the results prove that Bitcoin attention is an important pricing factor for carbon futures market.
The research purpose of this paper is to obtain an algorithm model with high prediction accuracy for the price of Bitcoin on the next day through random forest regression and LSTM, and to explain which variables have influence on the price of Bitcoin. There is much prior literature on Bitcoin price prediction research, and the research methods mainly revolve around the ARMA model of time series and the LSTM algorithm of deep learning. Although it cannot be proved by the Diebold–Mariano test that the prediction accuracy of random forest regression is significantly better than that of LSTM, the prediction errors RMSE and MAPE of random forest regression are better than those of LSTM. The changes in the variables that determine the price of Bitcoin in each period are also obtained through random forest regression. From 2015 to 2018, three US stock market indexes, NASDAQ, DJI, and S&P500 and oil price, and ETH price have impact on Bitcoin prices. Since 2018, the important variables have become ETH price and Japanese stock market index JP225. The relationship between accuracy and the number of periods of explanatory variables brought into the model shows that for predicting the price of Bitcoin for the next day, the model with only one lag of the explanatory variables has the best prediction accuracy.
This article examines the asymmetric volatility spillover effects between Bitcoin and alternative coin markets at the disaggregate level. We apply a frequency connectedness approach to the daily data of 11 major cryptocurrencies for the period from 1 September 2017 to 2 March 2022. We try to uncover the existence of the “fear of missing out” psychological effect and “pump-and-dump schemes” in the crypto markets. To do that, we estimate the volatility spillovers from Bitcoin to altcoin and the cryptos’ own risk spillovers during bull and bear markets. The spillover results from Bitcoin to altcoin provide mixed results regarding the presence of this theory for major cryptocurrencies. However, the empirical findings carried out by the cryptos’ own spillover effects fully confirm the existence of a fear-of-missing-out effect and pump-and-dump schemes in all cryptocurrencies except for USDT.
Orlando Telles Souza, João Vinícius de França Carvalho
Purpose This study aims to analyze the efficient market hypothesis (EMH) of cryptocurrencies on multiple platforms by observing whether there is a discrepancy in the levels of efficiency between different exchanges. Additionally, EMH is tested in a multivariate way: whether the prices of the same cryptocurrencies traded on different exchanges are temporally related to each other. ADF and KPSS tests, whereas the vector autoregression model of order p – VAR(p) – for multivariate system. Findings Both Bitcoin and Ethereum show efficiency in the weak form on the main platforms in each market alone. However, when estimating a VAR(p) between prices among exchanges, there was evidence of Granger causality between cryptocurrencies in all exchanges, suggesting that EMH is not adequate due to cross information. Practical implications It is essential to assess the cryptocurrency market in a multivariate way, not only to favor its maturation process, but also to promote a broad understanding of its inherent risks. Thus, it will be possible to develop financial products that are actively managed in a more sophisticated cryptocurrency market. Social implications There is a possibility of performing arbitrage on different exchanges and market assets through cross-exchanges. Thus, emphasizing the need for regulation of exchanges in the digital asset market, as an eventual price manipulation on a single platform can impact others, which generates various distortions. Originality/value This study is the first to find evidence of cross-information for the same (and other) cryptocurrencies among different exchanges.
Luca Mungo, Silvia Bartolucci, Laura Alessandretti
Abstract Since the introduction of Bitcoin in 2009, the dramatic and unsteady evolution of the cryptocurrency market has also been driven by large investments by traditional and cryptocurrency-focused hedge funds. Notwithstanding their critical role, our understanding of the relationship between institutional investments and the evolution of the cryptocurrency market has remained limited, also due to the lack of comprehensive data describing investments over time. In this study, we present a quantitative study of cryptocurrency institutional investments based on a dataset collected for 1324 currencies in the period between 2014 and 2022 from Crunchbase, one of the largest platforms gathering business information. We show that the evolution of the cryptocurrency market capitalization is highly correlated with the size of institutional investments, thus confirming their important role. Further, we find that the market is dominated by the presence of a group of prominent investors who tend to specialise by focusing on particular technologies. Finally, studying the co-investment network of currencies that share common investors, we show that assets with shared investors tend to be characterized by similar market behaviour. Our work sheds light on the role played by institutional investors and provides a basis for further research on their influence in the cryptocurrency ecosystem.
Lennart Ante, Ingo Fiedler, Jan Marius Willruth, Fred Steinmetz
This study reviews the current state of empirical literature on stablecoins. Based on a sample of 22 peer-reviewed articles, we analyze statistical approaches, data sources, variables, and metrics, as well as stablecoin types investigated and future research avenues. The analysis reveals three major clusters: (1) studies on the stability or volatility of different stablecoins, their designs, and safe-haven-properties, (2) the interrelations of stablecoins with other crypto assets and markets, specifically Bitcoin, and (3) the relationship of stablecoins with (non-crypto) macroeconomic factors. Based on our analysis, we note future research should explore diverse methodological approaches, data sources, different stablecoins, or more granular datasets and identify five topics we consider most significant and promising: (1) the use of stablecoins in emerging markets, (2) the effect of stablecoins on the stability of currencies, (3) analyses of stablecoin users, (4) adoption and use cases of stablecoins outside of crypto markets, and (5) algorithmic stablecoins.
Dora Almeida, Andreia Dionísio, Isabel Vieira, Paulo Ferreira
Cryptocurrencies are relatively new and innovative financial assets. They are a topic of interest to investors and academics due to their distinctive features. Whether financial or not, extraordinary events are one of the biggest challenges facing financial markets. The onset of the COVID-19 pandemic crisis, considered by some authors a "black swan", is one of these events. In this study, we assess integration and contagion in the cryptocurrency market in the COVID-19 pandemic context, using two entropy-based measures: mutual information and transfer entropy. Both methodologies reveal that cryptocurrencies exhibit mixed levels of integration before and after the onset of the pandemic. Cryptocurrencies displaying higher integration before the event experienced a decline in such link after the world became aware of the first cases of pneumonia in Wuhan city. In what concerns contagion, mutual information provided evidence of its presence solely for the Huobi Token, and the transfer entropy analysis pointed out Tether and Huobi Token as its main source. As both analyses indicate no contagion from the pandemic turmoil to these financial assets, cryptocurrencies may be good investment options in case of real global shocks, such as the one provoked by the COVID-19 outbreak.
This study aimed to explain the relationship between bitcoin and nonfungible tokens (NFTs) to determine if the NFT is an alternative investment to bitcoin or a complement during oil price uncertainty. The results showed a comovement between NFT and bitcoin prices. However, after excluding the effect of oil prices and using the partial wavelet coherence test, the results changed and the comovements disappeared: bitcoin and NFT became two separate assets that are affected by different variables. Moreover, oil price has more impact on bitcoin than NFT in the medium and long run. However, these results indicate that the change in oil prices, to some extent, is not considered a strong influence on the crypto market. Nevertheless, a significant rise in crude oil prices leads to a significant change in the comovement between crypto assets and they become interrelated.
This paper aims to investigate the role of Bitcoin and gold in equity portfolio formation.The dynamic relationships among four asset classes: Bitcoin, gold, equities, and bonds are examined, using Thai data from April 30, 2013 to February 27, 2021.The dynamic conditional correlations based on the DCC-GARCH model show that stock-gold correlations are generally negative while stock-Bitcoin correlations are close to zero.Interestingly, stock-bond correlations display the highest value over time.The spillover indices also show that gold and Bitcoin are less connected with stock while bonds receive the largest spillover from stock.To formally test which assets can be used as a safe haven against stock, dummy variable regression models with three different dependent variables: namely asset returns, DCCs, and pairwise spillovers are estimated.The results from the dummy variable regressions reveal that only gold acts as a safe haven for Thai equity portfolio.Moreover, Bitcoin and bonds tend to provide weaker diversification benefits than gold.
This report attempts to look into the future of the global monetary system. The history and development of fiat currencies is reviewed, their strengths and weaknesses are presented, causing market stability or instability and recession, respectively. Attention is paid to the global processes affecting the traditional money markets and the accumulated experience of the institutions to deal with the crises. The increasing digitization in the industry, the economy and the daily life of every single person inevitably gives rise to the need for the use of digital currency to guarantee transactions between individual parties. The idea for these still unrealized needs was born nearly 40 years ago, and the first realization took place in 1996, when e-gold was born. Today, we have hundreds of cryptocurrencies that still operate in an unregulated market, and their legal status still varies from country to country. The desire of individual countries and banking institutions to start their legally regulated use by already developing and testing their own currencies is presented.
This article quantifies the correlation between Bitcoin and NVIDIA using the DCC-GARCH model during the period of 2020-2023. We analyzed data from investing.com for this research. Bitcoin is a cryptocurrency based on blockchain technology, which involves mining by solving complex cryptographic puzzles. Mining refers to the process of verifying and recording Bitcoin transactions through computation, and acquiring newly generated Bitcoins as a contribution to network security and the distributed consensus mechanism. Therefore, it is important to understand the correlation between Bitcoin and graphics cards, especially with the expansion of the virtual currency market. Determining the correlation between Bitcoin mining and graphics cards can help miners optimize their hardware choices, investors better understand market potential, and manufacturers produce and develop graphics cards according to market demand. Due to the high computational requirements of Bitcoin mining, traditional central processing units (CPUs) are not well-suited for this task. On the other hand, graphics cards (graphics processing units, GPUs) have become the preferred hardware for Bitcoin mining due to their highly parallel computing capabilities. Consequently, we hypothesize the existence of a correlation between Bitcoin and graphics cards, which is further validated in subsequent sections.
The pandemic that hit the world in 2020 has left unforeseeable consequences for the entire world economy. Bitcoin and gold are currencies whose prices have risen despite the crisis period. The results of the research, using Spearman's correlation coefficient, showed a statistically significant relationship between the movement of the price of bitcoin and the price of gold, which can be the basis for predicting the movement of the price of gold in the future, based on the movement of the price of bitcoin. A significant relationship was found between the movement of the bitcoin price and the increase in the number of users of bitcoin wallets, which clearly indicates an increase in the volume of trade in this currency and a wider representation of this currency. Theoretical research of behavioral economics has confirmed the hypothesis that when the financial system is exposed to a crisis, bitcoin and gold will have the characteristics of a "safe haven asset" which can be explained by the principles of behavioral economics.
Since Bitcoin came into the world, modelling and analyzing the underlying characteristics of Bitcoin has attracted increasing attention. This paper uses a framework including decomposition, reconstruction and extraction method (DRE) to analyze price fluctuations based on ultra-high-frequency data from Dec.1, 2019, to Nov.30, 2021. First, the ensemble mode decomposition (EMD) is employed to decompose the Bitcoin hourly spot price into 13 intrinsic mode functions (IMF) plus a residual. Second, the IMFs are reconstructed into high-frequency components, low-frequency components and a trend based on fine-to-coarse reconstruction. Furthermore, the intraday volatility analysis based on LM test is applied on 15-minutes frequency data to detect discontinuous jump arrivals and extract jump from realized quadratic variation. Empirical results show that three components of reconstruction can be identified as short term fluctuations process caused by microstructure noise, the shocks affected by major events, and a long-term trend based on inelastic supply and rigid demand. We find that approximately 40% of jumps can be matched with the news from the public news database (Factiva), and the jump sizes are larger than that of stock markets. This finding indicates that the Bitcoin market has more irregularly noise and unforeseen shocks from unscheduled events.