In recent years, Bitcoin and other cryptocurrencies like Ethereum and Dogecoin have emerged as important asset classes in general, and diversification and hedging instruments in particular. The recent COVID-19 pandemic has provided the chance to examine and assess cryptocurrenciesâ behavior during extremely stressful times. The methodology of this study is based on an estimate using the ARDL model from 22 January 2020 to 12 March 2021, allowing us to analyze the long-term and short-term relationship between cryptocurrencies and COVID-19. Our results demonstrate that there is cointegration between the chosen cryptocurrencies in the market and COVID-19. The results indicate that Bitcoin, ETH, and DOGE prices were affected by COVID-19, which means that the pandemic seriously affected the three cryptocurrency prices.
In the burgeoning field of bitcoin research, a cohesive understanding of how knowledge and insights have evolved over time is lacking. This study aims to address this gap through an exploration of 4123 academic articles pertaining to bitcoin. Utilizing co-word analysis and main path analysis (MPA), it uncovers key themes and seminal works that have substantially influenced the fieldâs progression. The identified clusters, including safe haven, internet of things (IoT), proof of work (PoW), market efficiency, sentiment analysis, digital currency, and privacy, shed light on the multifaceted discourse surrounding bitcoin. The MPA, incorporating both forward and backward local paths, traces an evolving narrative, starting from an in-depth exploration of bitcoinâs structure, anonymity, and contrasts against traditional financial assets. It tracks the shift in focus to broader market dynamics, volatility, speculative nature, and reactions to economic policy fluctuations. The analysis underscores the transformation of bitcoin research, from its beginnings as a decentralized, privacy-oriented currency to its role in global economics and green financing, revealing a complex narrative of an innovative financial instrument to a multifaceted entity. Implications drawn from this analysis include the need for further research on the potential integration of bitcoin within emerging technologies like AI and cybersecurity, the implications of bitcoinâs interplay with traditional financial systems, and the environmental impacts of bitcoin and blockchain utilization. Overall, the current study not only enhances our understanding of the bitcoin field but also charts its dynamic evolution and stimulates further academic inquiry.
Abstract Bitcoin enthusiasts argue that it is free from central banks decisions and it is a hedge against inflation. Using high-frequency monetary surprises associated with decisions made by the Fed and the ECB, I show that these claims are not supported by the data. Bitcoin systemically reacts to monetary and central bank information shocks. I find that these reactions vary over time: not only by changing the magnitude but sometimes sign of reaction. Fedâs disinflationary shocks increase Bitcoin price, while the ECBâs decrease, hence providing little support for it as an inflation hedge.
Abstract This article examines the causal relationship between stock indices and cryptocurrencies during the current war between Russia and Ukraine. The econometric investigation runs from February 24, 2022 to April 12, 2023, focusing on seven stock market indices (S&P500, DAX, CAC40, Nikkei, TSX, MOEX and PFTS) and seven cryptocurrencies (Bitcoin, Ethereum, Litcoin, Dash, Ripple, DigiByte and XEM). In this article, we try to understand how investors react to fluctuations in financial assets to seek safe havens in crypto currencies. We used dynamic causality in the Granger (1969) sense to detect a possible causal relationship in the short term, and seven models to estimate the long-term relationship between cryptocurrencies and financial assets. The causal relationship between financial market indexes and cryptocurrency coins in the short run indicate that three famous cryptocurrencies (BITCOIN, ETHEREUM, RIPPLE) and the two digital asset with minor popularity (XEM, Digibyte) are impacted by the German, Russian and Ukrainian stock markets. In the long-run we found a positive and significate effect of the American, Canadian, French and Ukrainian stock market indexes on Bitcoin. Thus, the stability of the traditional financial markets during the current war period can be explained on the one hand by investorsâ fears of an unstable business climate, and on the other hand, by speculatorsâ sentiment towards new electronic products which are perceived as hedging instruments and a safe haven in the face of the conflict between Ukraine and Russia. JEL Classifcation: C5 ¡ C22 ¡ G1
Open access
Environmental and Biological Research in Conflict Zones
In this paper, a hybrid of a Wavelet DecompositionâGeneralised Auto-Regressive Conditional HeteroscedasticityâExtreme Value Theory (WD-ARMA-GARCH-EVT) model is applied to estimate the Value at Risk (VaR) of BitCoin (BTC/USD) and the South African Rand (ZAR/USD). The aim is to measure and compare the riskiness of the two currencies. New and improved estimation techniques for VaR have been suggested in the last decade in the aftermath of the global financial crisis of 2008. This paper aims to provide an improved alternative to the already existing statistical tools in estimating a currency VaR empirically. Maximal Overlap Discrete Wavelet Transform (MODWT) and two mother wavelet filters on the returns series are considered in this paper, viz., the Haar and Daubechies (d4). The findings show that BitCoin/USD is riskier than ZAR/USD since it has a higher VaR per unit invested in each currency. At the 99% significance level, BitCoin/USD has average values of VaR of 2.71% and 4.98% for the WD-ARMA-GARCH-GPD and WD-ARMA-GARCH-GEVD models, respectively; and this is slightly higher than the respective 2.69% and 3.59% for the ZAR/USD. The average BitCoin/USD returns of 0.001990 are higher than ZAR/USD returns of â0.000125. These findings are consistent with the mean-variance portfolio theory, which suggests a higher yield for riskier assets. Based on the p-values of the Kupiec likelihood ratio test, the hybrid model adequacy is largely accepted, as p-values are greater than 0.05, except for the WD-ARMA-GARCH-GEVD models at a 99% significance level for both currencies. The findings are helpful to financial risk practitioners and forex traders in formulating their diversification and hedging strategies and ascertaining the risk-adjusted capital requirement to be set aside as a cushion in the event of the occurrence of an actual loss.
The valuation of cryptocurrencies is important given the increasing significance of this potential asset class. However, most state-of-the-art cryptocurrency valuation methods only focus on one of the fundamental factors or sentiments and use out-of-date data sources. In this study, a robust cryptocurrency valuation method is developed using the up-to-date datasets. Using various panel regression models and moving-window regression tests, the impacts of fundamental factors and sentiments in the valuation of cryptocurrencies are explored with data covering from January 1, 2009 to April 30, 2023. The research shows the importance of sentiments and suggests that fear and greed index can indicate when to make cryptocurrency investment, while Google search interest of cryptocurrency are crucial when choosing the appropriate type of cryptocurrency. Moreover, consensus mechanism and initial coin offering have significant effects on cryptocurrencies without stablecoins, while their impacts on cryptocurrencies with stablecoins are insignificant. Other fundamental factors, such as the type of supply and the presence of smart contracts, do not have a significant influence on cryptocurrency. Findings from this study can enhance cryptocurrency marketisation and provide insightful guidance for investors, portfolio managers and policymakers in assessing the utility level of each cryptocurrency.
M.A. Ehyaei, A. Tofighi, Marc A. Rosen, Hamed Afshari ¡ 6 authors
Bitcoin, the first decentralized digital currency introduced by an anonymous person or group since 2008, has attracted worldwide attention. A significant number of economists have introduced Bitcoin as a new phenomenon in the 21st century that could reduce global inflation. Given the tens of thousands of digital currencies that have emerged since the advent of Bitcoin and its price growth trend over more than a decade, which are signs of the growth of this business. In addition to being money, Bitcoin has always been considered a tool for investing and storing value, which is why it is called digital gold. One of the most important problems in the production or extraction of Bitcoins is the high-power consumption by miners. If the energy sources of electricity generation are supplied by non-renewable energy sources, in addition to emitting air pollutant gases, it will increase greenhouse gases and consequently contribute to climate change. In this research, based on the idea of the authors, which is that the economic support of Bitcoin is energy, a strategy for producing Bitcoin from renewable energy sources is considered. First, the amount of electrical energy consumption by Bitcoin production is calculated based on statistical data, and then based on the price of electricity in different countries of the world and its global average, the base price of Bitcoin is calculated. In the following, four scenarios are proposed for the production of Bitcoin by electricity supplied from non-renewable energy sources. These scenarios include coal-fired steam power plants, natural gas-fired power plants, natural gas/oil gas-fired power plants, and dual-cycle (steam and gas cycles) natural gas-fired power plants. Based on the amount of electricity required to produce one Bitcoin, the amount of pollutants emitted to produce Bitcoin and its social costs are calculated. These costs should be added to the base cost of Bitcoin production if non-renewable energy sources are used to produce Bitcoin. Then, renewable energy sources for Bitcoin production based on the price of electricity generated by renewable energy sources are examined. Based on the analyses, how to choose the best renewable energy source to produce Bitcoin is presented as a scenario. This article briefly answers two key questions: 1. At what price of Bitcoin is it cost-effective for governments to produce it? 2. What is the best renewable energy source to produce it? These two questions can be useful in creating a roadmap and strategy for economists and governments.
Andreea-Elena DrÄgnoiu, Moritz Platt, Zixin Wang, Zhixuan Zhou
The energy consumption of popular cryptocurrencies varies greatly: cryptocurrencies based on proof-of-work (e.g. Bitcoin) consume much more electricity than their counterparts that use alternative consensus mechanisms, such as proof-of-stake (e.g. Ethereum). Nevertheless, proof-of-work cryptocurrencies dominate the market. We investigate whether energy labelling, i.e., displaying electricity consumption information on centralized exchanges, influences consumersâ product preferences. We conduct a control/treatment study: during this study, participants with an interest in cryptocurrencies (N = 200) are presented with a fictitious cryptocurrency exchange user interface. The treatment group is shown a user interface that displays energy labels, while the control group receives no information related to electricity consumption. Participants then declare how likely they are to acquire particular cryptocurrencies. We measure the treatment effect and find a significant negative correlation (p = 0.002) between being exposed to energy labels and expressing a strong preference for energy-ineďŹcient cryptocurrencies. Based on this finding, we reflect on the sustainability issues of cryptocurrencies and discuss how energy labelling on centralized exchanges can be applied to nudge investors away from energy-ineďŹcient cryptocurrencies. This indicates that regulators would be well advised to consider energy labelling to address the adverse climate impacts of cryptoassets.
We examine the reactions of the cryptocurrency market to two events that occurred during the escalation of the RussiaâUkraine war in February 2022. Using hourly data, we find that the escalation exerted a negative influence on both liquidity and returns. Interestingly, the actual escalation triggered a more pronounced drop than the threat of escalation shortly before. This contrasts with the stock market, where threats of geopolitical events are found to have a greater impact. Post-escalation, we observe indications of increased demand for cryptocurrencies, potentially as a means to circumvent Western sanctions imposed on Russia or to provide aid to Ukraine.
The main purpose of this paper is to investigate whether the cryptocurrency market affects financial stability and economic growth of India. The study used quarterly data on bitcoin, financial stability, inflation rate, real GDP, economic volatility uncertainty, exchange rate, and market volatility index for the period 2015Q1-2021Q4. The robustness of the findings was confirmed by the fully modified OLS (FMOLS) and canonical cointegration regression (CCR). The study results demonstrated that an increase in cryptocurrency investments will affect the financial stability of India significantly. Each 1% increase in the cryptocurrency would reduce the financial stability by 5% approximately. However, there was a marginal effect of cryptocurrency on economic growth. The results also found that exchange rate volatility and inflationary pressure would also deteriorate the financial stability of the country. Furthermore, the results also identified positive and significant cointegration between economic growth and financial stability. Due to most transactions in the economy being done through the financial system, it is paramount for economic growth. Going forward, aggressive monetary policy tightening, volatility in capital flows and exchange rates, deanchoring of inflation expectations, faltering in the economic recovery, disruptions due to global supply chains and climate change will be the major risks to the financial stability and economic growth of India.
Has there been a linkage mechanism between the prices of Bitcoin and traditional wealth preservation investment tools, that is, Bitcoin may serve as an investment substitute when other investment tool markets are sluggish, or can also benefit from it when the overall investment market is hot. The price of Bitcoin exhibits extremely unstable characteristics, as it can double its value dozens of times in a very short period of time or return to its starting point in a single day. The rapid rise and short duration of Bitcoin's price show us its infinite potential. Through research, this paper finds that the price of Bitcoin fluctuates greatly, while the U.S. Dollar Index and the S&P 500 index are basically horizontal, and their volatility is relatively small. Therefore, Bitcoin may be used as a speculative product, and there is a lot of speculative behavior in the market. When the investment attributes of Bitcoin dominate, an increase in economic policy uncertainty will significantly suppress investor sentiment and cause Bitcoin prices to decline. In addition, its impact on the world financial system is also increasing.
In view of the need for portfolio diversification, we investigate the interlinkages between a private equity ETF and a set of high-demand asset classes including bonds, equities, crude oil, gold, commodities, currency, Bitcoin, and shipping within a spillover framework. For this objective, we apply the enhanced modification of the Diebold and Yilmaz approach for the period 1 January 2010 to 31 January 2023. The empirical findings indicate a modest degree of connectedness among the investigated markets, whereas volatility spillovers showed acceleration during tumultuous periods. In addition, we assess the capacity of private equities for hedging, for the whole sample period and during COVID-19 infectious disease, in order to suggest investors for potential portfolio restructures. Results demonstrate that the short position in the volatility of private equity ETF can result in strong hedging effectiveness for investors holding long positions in Bitcoin, shipping, bonds, and crude oil. JEL Classification: C32, C58, G11, G15
Yunfei Yang, Jiamei Xiong, Lei Zhao, Xiaomei Wang ¡ 6 authors
Cryptocurrency prices have the characteristic of high volatility, which has a specific resistance to cryptocurrency price prediction. Therefore, the appropriate cryptocurrency price predictive method can help reduce the investment risk of investors. In this study, we proposed a novel prediction method using a fractional grey model (FGM (1,1)) to predict the price of blockchain cryptocurrency. Specifically, this study established the FGM (1,1) through the closing price of three representative blockchain cryptocurrencies (Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC)). It adopted the PSO algorithm to optimize and obtain the optimal order of the model, thereby conducting prediction research on the price of blockchain cryptocurrency. To verify the predictive precision of the FGM (1,1), we mainly took MAPE, MAE, and RMSE as the judging criteria and compared the modelâs predictive precision with the GM (1,1) through experiments. The research results indicate that within the data range studied, the predictive accuracy of the FGM (1,1) in the closing price of BTC, ETH, and LTC has reached a âhighly accurateâ level. Moreover, in contrast to the GM (1,1), the FGM (1,1) outperforms predictive capability in the experiments. This study provides a feasible new method for the price prediction of blockchain cryptocurrency. It has specific references and enlightenment for government departments, investors, and researchers in theory and practice.
Iulia Cioroianu, Shaen Corbet, Charles Larkin, Les Oxley
Using estimated sentiment indices based on CBDC-related social media posts, and testing for the effects of regulatory-related announcements upon blockchain and cryptocurrency-related funds, this research presents two key findings: first, the continued evolution of the pricing structures of digital finance products to respond to such perceived threats constitutes a further evolutionary point in the product's life-cycle. However, secondly, the very fact that returns fall while volatility increases, indicates a largely negative market response to the threat of potential external regulation of cryptocurrencies in the future. The nature of this negative response validates concerns that anonymity continues to be a central attractive feature for cryptocurrency stakeholders, further verifying the necessity for third-party oversight.
Blanka Ĺ krabiÄ PeriÄ, Petar SoriÄ, Ivana JerkoviÄ
This paper aims to examine the behavioural determinants of Bitcoin trading volume within a cross-country framework of 14 world economies plus the Eurozone. We introduce a basic taxonomy of behavioural indicators, distinguishing between consumer confidence, economic policy uncertainty (EPU), and indicators of financial volatility. Our estimations reveal that the Bitcoin trading volume can be predicted more accurately by EPU than by any other class of indicators. Finally, we identify the COVID-19 shock as a catalyst for a psychologically-driven Bitcoin market and find evidence that Bitcoin was a macro hedging instrument in the pandemic. To obtain our results, we conducted a panel Granger causality test, employing the Least Squares Dummy Variables (LSDV) estimator. Contrary to previous research, we found that market fundamentals (industrial production and equity market volume) became significant drivers of Bitcoin trading during the pandemic. This conclusion was preserved when we used the LSDV corrected estimator, which is more suitable for panels with a smaller time dimension. Apart from the practical implications for traders, this paper provides researchers with detailed steps for applying Granger causality testing in panel data settings.
Pairs trading is a popular quantitative trading strategy with the advantage of a similarity in price movement to financial assets. Assuming that the price spreads of trading pairs are mean-reverting, this strategy exploits the disequilibrium in financial markets to find arbitrage investment opportunities. Pairs trading has been widely applied to stock, ETF, and commodity markets. However, the effectiveness of this method for cryptocurrency markets has yet to be properly explored. Therefore, we examine the profitability of pairs trading for 26 cryptocurrencies traded on the Binance exchange at high frequencies of 1, 5, and 60 min. In addition to the traditional statistical methods of distance, correlation, cointegration, and stochastic differential residual (SDR), we focus on two evolutionary algorithms: genetic algorithm (GA) and non-dominated sorting genetic algorithm II (NSGA-II). During the 79-trading-day period from 11 January to 31 March 2018, NSGA-II showed the best results at all frequencies, with an average return of 2.84%. Among the statistical models, SDR ranks first, whereas Correlation ranks last, with average returns of 1.63% and â0.48%, respectively. The z-test results show that the models are statistically significantly different. We propose NSGA-II as the best candidate for use in pairs trading strategies in cryptocurrency markets.
Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, the downside risks are important to consider and model. As a result, the profitability of crypto market operations depends on the predictability of price volatility. Predictive models that can successfully explain volatility help to reduce downside risk. In this paper, we investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregressive score (GAS) models. We apply these models to five of some of the largest market capitalization cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Steller, respectively). The forecasts are evaluated using various backtesting and model confidence set (MCS) techniques. To create the best VaR forecast model, a weighted aggregative technique is used. The findings demonstrate that the quantile-based models using a weighted average method have the best ability to anticipate the negative risks of cryptocurrencies.
It is important to determine the network effects and store-of-value feature of cryptocurrencies due to the argument that it could be considered as a new âasset classâ. Current studies on cryptocurrencies' network effects mainly focused on using Metcalfe's Law to evaluate the relationship between cryptocurrency prices and the squared number of active wallets addresses. In terms of cryptocurrencies' store-of-value features, previous studies primarily compared daily volatility of limited number of popular cryptocurrencies to Gold. Extant studies are also based on out-of-date data. This research extends the literature by using up-to-date daily data of a sample of the top 100 cryptocurrencies covering 2010â2023 to explore the network effects and the store of value characteristics of a wide range of cryptocurrencies. Firstly, we used nonlinear regression models to examine the relationship between cryptocurrency prices and active wallets addresses, the number of transactions and circulations. Secondly, to deepen our understanding of the store-of-value features of cryptocurrencies, we used a combination of GARCH models and time series analysis to explore the volatility in the daily returns of the sampled cryptocurrencies. Findings indicate that at least one of the network factors (i.e., active wallets addresses, the number of transactions, and number of circulation supply) have a significant effect on cryptocurrency prices. The study also finds that stable coins have comparable daily volatility as Gold, while only mature cryptocurrencies, such as PAXG, Bitcoin, Ethereum, BNB and LINK, demonstrate strong correlation with Gold. Bitcoin also showed a high positive time-series correlation with 24 of the 42 cryptocurrencies. Findings from this study provide important insights to investors, market analysts, regulators and other stakeholders on the marketisation and the store of value potentials of cryptocurrencies.
Over time, cryptocurrencies have experienced a widespread adoption, with bitcoin emerging as the most prominent example. In an increasingly uncertain world, the significance of possessing a stable store of value, traditionally fulfilled by gold, has escalated. Bitcoin has been often referred to as a digital equivalent of gold. Hence, this study primarily focuses on analyzing the price dynamics of this particular cryptocurrency. A comprehensive literature review will be employed to examine the regulatory obstacles encountered within the cryptocurrency market. Additionally, considering the contentious nature of this field, special attention will be devoted to the clash of perspectives surrounding this innovation. Subsequently, concentrating on the period 2016-2021, this paper will investigate the factors that define a risk-weighted investment, utilizing the Sharpe ratio and Sortino ratio. However, there has been significant volatility in the price of Bitcoin in 2020-2021, and our research fills a gap in the relationship between Bitcoin returns and risk in the post-2016 period. Overall, the analysis concludes that bitcoin exhibits highly turbulent investment characteristics. Despite its substantial price appreciation, the findings indicate that bitcoin displays significant volatility. Consequently, selecting this investment alternative entails considerable risks. Based on our results, there were years between 2016-2021 when bitcoin was a good investment, but in most cases its returns were associated with excessive volatility and risk. For this reason, it is not recommended for risk-averse rational investors.
Cryptocurrencies have obtained a crucial position in the international financial landscape. The cryptocurrency market has been perceived as a highly volatile market since the inception of Bitcoin. This study investigates the relevant performance of extreme value models (EVM) in estimating the Value-at-Risk (VaR) of Bitcoin and Ethereum returns. The extreme value mixture models, GPD-Normal-GPD (GNG) and GPD-KDE-GPD models are fitted to the returns of Bitcoin and Ethereum and the Kupiec likelihood backtesting procedure is performed on the VaR estimates to assess the fits. Both modelsâ results showed that the fits were a much more decent representation of the observed data when compared to the Normal distribution. The backtesting results showed that the GPD-KDE-GPD modelâs fit was superior to that of the GPD-Normal-GPD for both sets of returns at all VaR risk levels except at the 99% level. The results of this study may assist with understanding the dynamics and risks associated with cryptocurrencies and can serve as a beneficial tool for decision-making and risk management to investors, traders, financial institutions and many other participants in the cryptocurrency ecosystem.