Cryptocurrencies are gaining more popularity due to their security, making counterfeits impossible. However, these digital currencies have been criticized for creating a large carbon footprint due to their algorithmic complexity and decentralized system design for proof of work and mining. We hypothesize that the carbon footprint of cryptocurrency transactions has a higher dependency on carbon-rich fuel sources than green or renewable fuel sources. We provide a machine learning framework to model such transactions and correlate them with the electricity generation patterns to estimate and analyze their carbon cost.
Samuel Asumadu Sarkodie, Maruf Yakubu Ahmed, Thomas Leirvik
The environmental sustainability of bitcoin is making waves in the empirical literature, yet, no study has thus far examined the financial determinants of bitcoin energy consumption and carbon footprint. Here, we use novel estimation methods comprising dynamic ARDL simulations and general-to-specific VAR to examine steady-state effects, cumulative impulse-response, and counterfactual shocks of bitcoin trade volume on bitcoin energy bitcoin carbon footprint to ensure genuine causal inferences. We observed an increase in bitcoin trade volume spur both carbon and energy footprint by 24% in the long-run, whereas a dynamic shock in trade volume escalates bitcoin energy and carbon footprint by 46.54%.
Abstract In this paper, we provide an in-depth analysis of the herding nature in the cryptocurrency market. We use the first 200 crypto coins data ranked based on market capitalization on January 1, 2020, to show the analysis. We illustrate the crypto investors' herding nature and intensity in different terms (by using daily, weekly, and monthly frequency data) and various states (high vs. low EPU states and high vs. low VIX states). We also demonstrate the magnitude of the herding effect on the next day's market returns in the cryptocurrency market.
Purpose – the main aim of this article is to identify cryptocurrencies suitable for investment and portfolio diversification. Research methodology – the methodology of empirical research includes methods of scientific literature analysis, statistical data analysis, multicriteria evaluation, correlation analysis. Findings – Bitcoin is the leading cryptocurrency, but this result could have been due to an exceptionally high market capitalization. Based on the results of the analysis, the inclusion of Bitcoin, Etherium and Dogecoin in the investment portfolio of S&P500, Euro Stoxx 50, DAX and CAC 40 indexes could be considered. Terra could be an interesting investment when considering the benefits of diversification. Research limitations – based on the results of the study, the inclusion of all studied cryptocurrencies in the investment portfolio could be considered in order to diversify the portfolio, taking into account their investment attractiveness. Practical implications – Cryptocurrencies attract investors not only because of the returns they receive, but also because of the absence of intermediaries, which allows them to reduce transaction costs. High returns are associated with high risks, so it is necessary to conduct as much research as possible to identify the benefits of cryptocurrencies and to find risk management strategies. One such benefit of cryptocurrencies highlighted in research is diversification. Originality/Value – the novelty of the study lies in evaluation of 10 selected cryptocurrencies according to different criteria using a multi-criteria valuation method to identify cryptocurrencies that are non-correlated or weakly correlated with traditional assets and the most suitable for investment and for portfolio diversification.
This paper examines the diversification role of socially responsible investments (SRI) during the COVID-19 pandemic. To assess the contribution to risk diversification and improved financial performance of SRI we analyze the effect of including clean energy equities in portfolios of conventional equities and other assets commonly considered as safe havens. We construct minimum variance portfolios for different rebalancing frequencies and by considering or restricting short positions. Two approaches are applied: AR-GARCH models to fit the marginal distributions of individual assets and DCC skew Student copula specifications to model the conditional dependencies among pairs via the Kendall's tau correlation measure. We provide evidence of the important role that SRI have played in diversifying and improving the financial performance of portfolios based on different securities such as traditional equities, Treasury bonds, gold, crude oil and Bitcoin.
Most financial signals show time dependency that, combined with noisy and extreme events, poses serious problems in the parameter estimations of statistical models. Moreover, when addressing asset pricing, portfolio selection, and investment strategies, accurate estimates of the relationship among assets are as necessary as are delicate in a time-dependent context. In this regard, fundamental tools that increasingly attract research interests are precision matrix and graphical models, which are able to obtain insights into the joint evolution of financial quantities. In this paper, we present a robust divergence estimator for a time-varying precision matrix that can manage both the extreme events and time-dependency that affect financial time series. Furthermore, we provide an algorithm to handle parameter estimations that uses the "maximization-minimization" approach. We apply the methodology to synthetic data to test its performances. Then, we consider the cryptocurrency market as a real data application, given its remarkable suitability for the proposed method because of its volatile and unregulated nature.
Xinchen Zhang, Linghao Zhang, Qincheng Zhou, Xu Jin
As a result of the fast growth of financial technology and artificial intelligence around the world, quantitative algorithms are now being employed in many classic futures and stock trading, as well as hot digital currency trades, among other applications today. Using the historical price series of Bitcoin and gold from 9/11/2016 to 9/10/2021, we investigate an LSTM-P neural network model for predicting the values of Bitcoin and gold in this research. We first employ a noise reduction approach based on the wavelet transform to smooth the fluctuations of the price data, which has been shown to increase the accuracy of subsequent predictions. Second, we apply a wavelet transform to diminish the influence of high-frequency noise components on prices. Third, in the price prediction model, we develop an optimized LSTM prediction model (LSPM-P) and train it using historical price data for gold and Bitcoin to make accurate predictions. As a consequence of our model, we have a high degree of accuracy when projecting future pricing. In addition, our LSTM-P model outperforms both the conventional LSTM models and other time series forecasting models in terms of accuracy and precision.
The Bitcoin mining process is energy intensive, which can hamper the much-desired ecological balance. Given that the persistence of high levels of energy consumption of Bitcoin could have permanent policy implications, we examine the presence of long memory in the daily data of the Bitcoin Energy Consumption Index (BECI) (BECI upper bound, BECI lower bound, and BECI average) covering the period 25 February 2017 to 25 January 2022. Employing fractionally integrated GARCH (FIGARCH) and multifractal detrended fluctuation analysis (MFDFA) models to estimate the order of fractional integrating parameter and compute the Hurst exponent, which measures long memory, this study shows that distant series observations are strongly autocorrelated and long memory exists in most cases, although mean-reversion is observed at the first difference of the data series. Such evidence for the profound presence of long memory suggests the suitability of applying permanent policies regarding the use of alternate energy for mining; otherwise, transitory policy would quickly become obsolete. We also suggest the replacement of 'proof-of-work' with 'proof-of-space' or 'proof-of-stake', although with a trade-off (possible security breach) to reduce the carbon footprint, the implementation of direct tax on mining volume, or the mandatory use of carbon credits to restrict the environmental damage.
Michael Demmler, Amilcar Orlian Fernández Domínguez
This article explores the concepts of cryptocurrencies and speculative bubbles, as Bitcoin’s price behaviour shares characteristics with speculative bubbles that have occurred in recent years. Using a quantitative research design, the study examines daily market prices for the period between 2013 and 2019. Statistical moments, return stationarity, TARCH-type model estimations and Supremum Augmented Dickey-Fuller and Generalised Supremum Augmented Dickey-Fuller tests are analysed. We find evidence for multiple speculative bubble tendencies in Bitcoin prices caused by speculation, which reached their maximum at the end of 2017. Our results are in line with recent studies, which characterise Bitcoin as both highly speculative and vulnerable to financial bubbles.
This study examines the effects of the epidemic and the price bubble on the effectiveness of the cryptocurrency market. In this Research, We collect the daily closing price of 5 cryptocurrencies from https://coinmarketcap.com/. The data was taken from 01 September 2017 to 14 December 2021 with a total data or sample of 1231 daily data from each currency or a total of 6155 samples from a total of all tested currencies. The five cryptocurrencies are Litecoin (LTC), Cardano (ADA), Ethereum (ETH), Ripple (XRP), and Bitcoin (BTC). To measure market inefficiency we use magnitude market inefficiency (MIM) and the study by Le Tran and Leirvik (2019) is used to establish the adjusted magnitude of market inefficiency (AMIM). In this study AMIMt is calculated on a daily frequency by using the daily closing price as the basis for calculation.We found that the three periods of the cryptocurrency bubble in the cryptocurrency market occurred in late 2017, early 2018, and July 2020. The cryptocurrency financial bubble had a lesser impact than the announcement of a worldwide pandemic being declared for COVID-19 on March 11, 2020. It is very likely that a bubble will occur during July 2020 related to the declaration that COVID-19 is a pandemic of global scope.
Yizhi Wang, Brian M. Lucey, Samuel A. Vigne, Larisa Yarovaya
Based on coverage of over 660m news stories from LexisNexis News & Business between 2015–2021, we provide two new indices around the growing area of Central Bank Digital Currency (CBDC): the CBDC Uncertainty Index (CBDCUI) and CBDC Attention Index (CBDCAI). We show that both indices spiked during news related to new developments in CBDC and in relation to digital currency news items. We demonstrate that CBDC indices have a significant negative relationship with the volatilities of the MSCI World Banks Index, USEPU, and the FTSE All-World Index, and positive with the volatilities of cryptocurrency markets, foreign exchange markets, bond markets, VIX, and gold. Our results suggest that financial markets are more sensitive to CBDC Uncertainty than CBDC Attention as proxy by these indices. These findings contain useful insights to individual and institutional investors, and can guide policymakers, regulators, and the media on how CBDC evolved as a barometer in the new digital-currency era.
This paper aims to analyze the effect of Bitcoin on portfolio optimization using mean-variance, conditional value-at-risk (CVaR), and Markov regime switching approaches. I assessed each approach and developed the next based on the prior approach's weaknesses until I ended with a high level of confidence in the final approach. Though the results of mean-variance and CVaR frameworks indicate that Bitcoin improves the diversification of a well-diversified international portfolio, they assume that assets' returns are developed linearly and normally distributed. However, the Bitcoin return does not have both of these characteristics. Due to this, I developed a Markov regime switching approach to analyze the effect of Bitcoin on an international portfolio performance. The results show that there are two regimes based on the assets' returns: 1- bear state, where returns have low means and high volatility, 2- bull state, where returns have high means and low volatility.
Abstract As a new type of currency introduced in the new millennium, cryptocurrency has established its ecosystems and attracts many people to use and invest in it. However, cryptocurrencies are highly dynamic and volatile, making it challenging to predict their future values. In this research, we use a multivariate prediction approach and three different recurrent neural networks (RNNs), namely the long short-term memory (LSTM), the bidirectional LSTM (Bi-LSTM), and the gated recurrent unit (GRU). We also propose simple three layers deep networks architecture for the regression task in this study. From the experimental results on five major cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), Tether (USDT), and Binance Coin (BNB), we find that both Bi-LSTM and GRU have similar performance results in terms of accuracy. However, in terms of the execution time, both LSTM and GRU have similar results, where GRU is slightly better and has lower variation results on average.
In this work, I studied whether news media sentiments have an impact on Bitcoin volatility. In doing so, I applied three different range-based volatility estimates along with two different sentiments, namely psychological sentiments and financial sentiments, incorporating four various sentiment dictionaries. By analyzing 17,490 news coverages by 91 major English-language newspapers listed in the LexisNexis database from around the globe from January 2012 until August 2021, I found news media sentiments to play a significant role in Bitcoin volatility. Following the heterogeneous autoregressive model for realized volatility (HAR-RV)—which uses the heterogeneous market idea to create a simple additive volatility model at different scales to learn which factor is influencing the time series—along with news sentiments as explanatory variables, showed a better fit and higher forecasting accuracy. Furthermore, I also found that psychological sentiments have medium-term and financial sentiments have long-term effects on Bitcoin volatility. Moreover, the National Research Council Emotion Lexicon showed the main emotional drivers of Bitcoin volatility to be anticipation and trust.
Abstract The Bitcoin futures market has grown rapidly since its 2017 introduction. Along with enabling institutional traders to access a regulated cryptocurrency product, futures provide a means to improve market efficiency by shorting Bitcoin. We examine trading behavior in Bitcoin futures utilizing the Commodity Futures Trading Commission Commitment of Traders report. Leveraged money traders tend to hold the largest positions, be net short, and their trading behavior plays a key role in the Bitcoin futures market. Our empirical results show that leveraged money traders display market timing ability, largely by adjusting their short positions. It seems that other trader types follow this “smart money” in adjusting their own positions in subsequent periods. We also demonstrate that it is possible to construct profitable trading strategies based on observed variations in leveraged money positions.
The Covid-19 crisis, or even the restrictions, quarantines, and lifestyle changes that it brought, occurred in the year 2020. Economic statistics mentioned the effects of the crisis. Stock exchanges around the world, for example, have experienced substantial collapses, leading in a drop in the value of various individuals' assets. During the Covid-19 crisis, this master's study attempts to understand the utility of cryptocurrencies for hedging and safe haven objectives. It's difficult to make consistent conclusions about the suitability of cryptocurrencies for hedging against financial market risks based on existing research. Previous results have varied greatly based on the model utilized, the time period, and the asset risk hedged. Usability for hedging purposes varies in general. In this study, we wrote an article based on the most popular cryptocurrencies in the world and their development mechanisms, history and other facts. The article also discusses the role and importance of cryptocurrencies as a means of payment in the future. Keywords: cryptocurrency, covid-19 crisis, bitcoin, ethereum, blockchain technology
Overview The aim of the International Conferences "Economic Scientific Research-Theoretical, Empirical and Practical Approaches"- (ESPERA), initiated in 2013 by the "Costin C. Kirițescu" National Institute for Economic Research (NIER) within the Romanian Academy is to present and evaluate the economic scientific research portfolio, to argue and substantiate the Romanian development strategies - including European and global best practices, to provide an opportunity for researches, practitioners, and academics interested in economic scientific research, both theoretical, practical and empirical discuss and exchange insightful research ideas. The 7th edition of the International Conferences “Economic Scientific Research-Theoretical, Empirical and Practical Approaches”- (ESPERA), under the title ”30 Years of Inspiring Academic Economic Research – From the Transition to a Market Economy to the Interlinked Crises of 21st Century” was organized virtually during 26th -27th November 2020, In Bucharest, Romania. The event, dedicated to the 30th anniversary of NIER and its economic research network of its return under the auspices of the Romanian Academy, will include a scientific program of wide diversity initiatives, bringing together researchers from all NIER institutes and centers, members of the Romanian Academy, Romanian academic researchers and also guests from other countries. The
Bu çalışmada temel piyasalar arasındaki volatilite yayılımları Diebold ve Yılmaz (2012) tekniğiyle araştırılmıştır. Temel piyasaları temsilen MSCI dünya endeksi, ABD 2 yıllık devlet tahvil faizi, dolar endeksi, ons altın, brent petrol ve bitcoin kullanılmıştır. Çalışmada 2 Ocak 2015 – 29 Haziran 2021 dönemine ait günlük verilerden elde edilen volatiliteler kullanılmıştır. Çalışmada, temel piyasalar arasındaki volatilite yayılım endeksinin %30,9 olduğu, faiz ve MSCI dünya endeksinin volatilite yayıcısı buna karşın dolar endeksi, altın, petrol ve bitcoinin volatilite alıcısı oldukları, faizin temel piyasalarda önemli volatilite yayıcısı olduğu, bitcoinin temel piyasalarla volatilite ilişkisinin zayıf olduğu ve temel piyasalar arasındaki volatilite yayılımlarının COVID-19 sürecinde yükseldiği belirlenmiştir. Elde edilen sonuçlar, portföy yönetimi, risk yönetimi, yatırımlar, ekonomi yönetimleri açısından kullanılabilirlik taşımaktadır.
Samuel Kwaku Agyei, Anokye M. Adam, Ahmed Bossman, Oliver Asiamah · 7 authors
We present a multi-scale and time-frequency analysis of the degree of integration and the lead-lag relationship between six cryptocurrencies (i.e., Bitcoin, Bitcoincash, Ethereum, Litecoin, Ripple, and Tether) and the cryptocurrency-implied volatility index (VCRIX). As a result, the wavelet techniques—bi-wavelet, partial wavelet, bivariate contemporary correlations (BCC), wavelet multiple correlations (WMC) and wavelet multiple cross-correlations (WMCC) are applied. Findings from the study provide that the interdependencies between the cryptocurrencies and VCRIX are high and mostly positive across investment horizons. Furthermore, the comovements between the cryptocurrencies designate long memory dynamics. The high comovements between cryptocurrencies are highly influenced by idiosyncratic shocks they possess rather than the VCRIX. In addition, the BCC and the WMC indicate that there is a high integration among all the cryptocurrencies. Categorically, the VCRIX could not lead or lag the interdependencies among the cryptocurrencies in the WMCC analysis. Findings from the study, therefore, divulge that investing in a single or few cryptocurrencies is highly risky due to the adverse impact of the VCRIX on individual cryptocurrencies. In general, investors should effectively hedge against volatilities in the cryptocurrency markets due to the significant predictive ability of VCRIX as an effective proxy.
Abstract We examine the interactions between stablecoins, Bitcoin, and a basket of altcoins to uncover whether stablecoins represent the investors’ demand for trading and investing into cryptoassets or rather play a role as boosting mechanisms during cryptomarkets price rallies. Using a set of instruments covering the standard cointegration framework as well as quantile-specific and non-linear causality tests, we argue that stablecoins mostly reflect an increasing demand for investing in cryptoassets rather than serve as a boosting mechanism for periods of extreme appreciation. We further discuss some specificities of 2017, even though the dynamic patterns remain very similar to the general behavior. Overall, we do not find support for claims about stablecoins being bubble boosters in the cryptoassets ecosystem.
The large-scale application of blockchain technology is an expected to be an inevitable trend. This study revolves around published papers and articles related to blockchain technology, relevance analysis and sorting through the retrieved documents with six core layers of blockchain: Application Layer, Contract Layer, Actuator Layer, Consensus Layer, Network Layer and Data Layer. Based on the analysis results, this study found that China's research is more towards the preference and application of landing and industry and smart cities with blockchain as the underlying technology. International research is more focused on the research of finance as the underlying technology of blockchain and tries to combine crypto assets with real industries, such as crypted assets and payment systems for traditional industries. This paper studies the impact of monetary entropy on cryptocurrencies in smart cities and uses the monetary entropy formula to measure the crypto-economic entropy. We use Kolmogorov entropy to describe the degree of chaos in the cryptocurrency market in a smart city. The study illustrates the current status of blockchain technology and applications from the perspective of cryptocurrency in a smart city. We find that smart cities and cryptocurrencies have a mutually reinforcing effect.
Cryptocurrencies can be considered as mathematical money. As the most famous cryptocurrency, the Bitcoin price forecasting model is one of the popular mathematical models in financial technology because of its large price fluctuations and complexity. This paper proposes a novel ensemble deep learning model to predict Bitcoin’s next 30 min prices by using price data, technical indicators and sentiment indexes, which integrates two kinds of neural networks, long short-term memory (LSTM) and gate recurrent unit (GRU), with stacking ensemble technique to improve the accuracy of decision. Because of the real-time updates of comments on social media, this paper uses social media texts instead of news websites as the source data of public opinion. It is processed by linguistic statistical method to form the sentiment indexes. Meanwhile, as a financial market forecasting model, the model selects the technical indicators as input as well. Real data from September 2017 to January 2021 is used to train and evaluate the model. The experimental results show that the near-real time prediction has a better performance, with a mean absolute error (MAE) 88.74% better than the daily prediction. The purpose of this work is to explain our solution and show that the ensemble method has better performance and can better help investors in making the right investment decision than other traditional models.