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.
B Sriman, Tamil Iniyal T, J Thasmiya, Taariq Ziyaadh J · 6 authors
India is rapidly moving towards the digitization of money in all aspects. Cryptocurrency has grown widely in India and around the world among investors for financial activities like buying, selling, and trading. According to the report submitted in 2021 by the United Nations Conference on Trade and Development, 7.3% of Indians owned cryptocurrency in 2021. In the past two years, i.e., 2020 and 2021, the value of global currencies have been falling due to the poor run of stock markets. So the investors found it very hard to cope with the economical issues. This in turn has led to a renewal of interest in digital currency. Our main target is to implement the efficient machine learning and deep learning- based models specifically Convolutional Neural Network (CNN), long short term memory(LSTM) and Gated Recurrent Units (GRU) to handle the price volatility of bitcoin and ethereum and to produce high accuracy.
J. Alvarez-Ramirez, Luísa Castro, Eduardo Rodríguez
The recent decade has witnessed a surge of cryptocurrency markets as innovative financial systems based strongly on digital emission, interchange and coding. The main characteristic is that cryptocurrencies are not subjected to the regulation of governments and financial institutions (e.g., central banks), such that their dynamics are determined solely by non-centralized mechanisms. Informational efficiency is a key issue for cryptocurrency markets since its fulfillment guarantees that all participants have access to the same information quality and that arbitrage conditions are discarded. This study evaluated the contribution of nonlinearities to the informational efficiency of the Bitcoin market for the period 2014–2022. Singular value decomposition (SVD) entropy together with shuffled and phase-randomized data in a rolling-window framework was used to capture randomness and nonlinear dynamics in Bitcoin returns. It was found that the contribution of nonlinearities to informational efficiency increases with the time scale, with a mean contribution of about 7.25% for long-time scales. This means that the Bitcoin market is only affected by weak nonlinearities, although these effects should be considered for forecasting and valuation.
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.
This paper studies two cryptocurrencies and finds that their prices can be estimated or forecasted better than their returns because returns being ratios of prices, do not always exhibit the economic relationship that may exist between two price series. However, average returns use multiple prices in their ratios that capture the economic behavior of the price series. Further, the forecasting performance of traditional preceding return models are compared with those of preceding average return models and the latter are found to generally give better results in terms of Root Mean Square Error (RMSE) and average return on investments (ARoIs).