Ahmed Bouteska, Petr Hájek, Mohammad Zoynul Abedin, Yizhe Dong
This study aims to examine whether the prices and returns of two cryptocurrencies, Dogecoin and Ethereum, are affected by Twitter engagement following the COVID-19 pandemic. We use the autoregressive integrated moving average with explanatory variables model to integrate the effects of investor attention and engagement on Dogecoin and Ethereum returns using data from December 31, 2020, to May 12, 2021. The results provide evidence supporting the hypothesis of a strong effect of Twitter investor engagement on Dogecoin returns; however, no potential impact is identified for Ethereum. These findings add to the growing evidence regarding the effect of social media on the cryptocurrency market and have useful implications for investors and corporate investment managers concerning investment decisions and trading strategies.
As a great innovation in virtual currency, bitcoins have the possibility to survive perpetually, although they are like gigantic bubbles. However, no matter whether bitcoins could survive or not, the technology used by bitcoins will exist and develop. There is a great possibility for bitcoins to be served in the intending currency, being issued, supported, and controlled by the government. Consequently, the research for bitcoins is meaningful. To explore the time relationship of bitcoins and give a prediction about the future price based on the given data, ARIMA and GARCH models are used in this paper. Although both of the two models failed to provide the accurate forecasts at the end of this research, they still proved the correlation within time series of bitcoins.
Bitcoin establishes itself as an investment asset and is often named the New Gold. This study, however, shows that the two assets are different in univariate and multivariate aspects. First, we construct GARCH, APARCH and APARCH-in-Mean models to analyze and compare conditional variance properties of Bitcoin and Gold, and find Bitcoin does not have the significant inverse leverage effect as Gold. Then we apply the BEKK-GARCH model to estimate time-varying conditional correlations between Bitcoin and Gold with other major market indexes. The results show that Bitcoin can not hedge the market risk, especially when a crash occurs. So we conclude that Bitcoin and Gold feature fundamentally different properties as assets and linkages to equity markets.
Yatırım, tasarruf sahiplerinin finansal sürdürülebilirliğin güvence altına alınmasını sağlayan önemli bir araçtır. Bu nedenle yatırım kararlarının belirlenmesi ve portföy oluşturma süreçleri güncelliğini yitirmeyen bir araştırma konusu olagelmiştir. Bu çalışmada son yıllarda çok sayıda tartışmaya konu olan Bitcoin’in portföyler için doğru bir alternatif olup olmadığı tartışılmaktadır. Portföyler oluşturulurken çeşitliliği artırmak için Dolar, Euro, Bitcoin, Bist100 ve Altın alternatif yatırım araçları ele alınmıştır. Portföyler eşit oranlı bir dağılıma sahip olacak şekilde beş yatırım aracının olası tüm kombinasyonlarından oluşturulmuştur. Yatırım kararı, çok kriterli karar verme problemi olarak ele alınmış ve değerlendirme için yıllık getiri göstergesi, yıllık değişim oranı ve varyans katsayısı olacak şekilde üç kriter belirlenmiştir. Kriterlerin ağırlıkları nesnel bir yaklaşım olan MEREC yöntemiyle hesaplanmış ve alternatif seçimi VIKOR yöntemiyle gerçekleştirilmiştir. Çalışmada, Bitcoin’in portföy çeşitlendirmek için uygun bir alternatif olduğu sonucuna ulaşılmıştır.
Many investors include cryptocurrencies as potential investment tools in their portfolios. Previous studies have mostly analyzed Bitcoin regarding its hedge and safe haven features. Although the cryptocurrency market has expanded far beyond Bitcoin, few studies have examined the interaction among all other cryptocurrencies and conventional financial assets. For this purpose, as the dependent variable, we included the cryptocurrency index to represent the cryptocurrency market, whereas international stocks, bonds, United States (US) dollars, gold, and commodities as independent variables in the analysis. The interactions among the variables were analyzed using the Granger causality tests. The analysis results revealed a two-way causality relationship between the cryptocurrency market and the bond markets, indicating that the cryptocurrency index can be used to predict bond prices and vice versa.
Tolgahan Tuglu, Canan Dağıdır Çakan, Mehmet Hanifi Ateş, Aleyna Uca
Cryptocurrencies have been attracting a significant amount of attention in the world since they were first launched in 2009. Pretending to be a decentralized finance solution, it brought out a new era in technology called blockchain. Even though the benefits did not come into action in daily routines for many to be aware of, the market and its variety kept growing. On the other hand, there are also a lot of concerns and unpredictability about the future of this technology. Especially the high energy consumption while generating blocks for mining cryptocurrencies and completing transactions is commonly being criticised. In this study, blockchain technology and the basics of mining and validation procedures such as proof of work (PoW) and proof of stake (PoS) processes will be explained, and the environmental effects of bitcoin mining will be investigated. In the perspective of environmental sustainability of cryptocurrencies, the improvement in usage of renewable energy and its side benefits will be overviewed for a better prediction on the blockchain technology future.
Bitcoin prices are highly volatile and have extreme upper tails of the return distributions. One important component of Bitcoin price jumps is that it does not follow a normal distribution. This present study aims to reduce the extreme value data available on Bitcoin into simple clusters based on extreme value returns. The study first measures the excessive volatility and then estimates the extreme value returns of Bitcoin between November 2013 and August 2022 to achieve this objective. For robustness checks, extreme value returns are estimated using both the Rogers and Satchell (RS) and the Variance Ratio (VRatio) estimators that embed jumps in the model. Further, K-means clustering is used to form clusters based on the estimated Bitcoin’s extreme value returns as the probable good days (extreme days), medium days, and bad days. The study observes that K-means clustering can explain 65 percent point return variability. The study findings will be highly useful for crypto investors, policymakers, and future studies in data mining.
Reza Nourmohammadi, Mahdi Arabian, Masoumeh Ghorbanpour, Mohammad M. Nazemi · 5 authors
As a cutting-edge technology, non-fungible tokens (NFT) have attracted a great deal of attention since 2021. Considering the numerous applications of these non-interchangeable digital assets in various industries and their tradability, NFTs have become an important element of many investors’ portfolios. Therefore, in order to evaluate NFTs and determine their main value, different tools must be used. The purpose of this study is to understand the dominant factors that influence the valuation of NFT assets. The purpose of this paper is to present a novel methodology for constructing a utility valuation model for NFTs as a whole. We will be able to analyze and diagnose the dynamics and performance of NFT markets using this model. We developed three models for scoring NFTs in this study, which can be used to speed up the evaluation process in three different dimensions.
The COVID-19 pandemic and the bearish market have led investors to find a safe-haven asset during this financial turbulence. Gold, US Dollar, and Bitcoin traditionally could be safe-haven assets in previous financial crises. However, safe-haven assets are mainly different during each market crash. Therefore, this paper aims to examine gold, US dollars, and Bitcoin as safe-haven assets during the COVID-19 market turmoil in several South East Asian countries such as Indonesia, Malaysia, Singapore, and the Philippines. All variables use daily data time series from January 2020 - September 2020. This study will conduct an empirical analysis using Generalized Autoregressive Conditional Heteroscedasticity (GARCH). Our result shows that during the COVID-19 pandemic, US Dollar could act as a safe-haven asset in Indonesia, Malaysia, and the Philippines. It implies that when the condition is uncertain during a pandemic, many investors switch their investments to US dollars in those three countries. On the other hand, gold and bitcoin are not safe-haven assets, but they could only act as hedging for several countries in South-East Asia.
This paper empirically assesses the ability of three putative stablecoins (two dollar-backed, Tether and USD Coin; and one gold-backed, Digix Gold) to mitigate the risk of facing severe losses (downside risk) of a traditional cryptocurrency portfolio. There are institutional features that induce cryptoinvestors to use stablecoins as diversifiers instead of withdrawing dollars or adding assets traditionally considered as safe havens, such as gold, crude oil, etc. Stablecoins, however, are not as stable as their name and collateralized peg suggest. A monthly rebalance experiment is conducted over an out-of-sample period considering higher order conditional moments when dynamically measuring the tail risk of cryptocurrency portfolios. The empirical evidence shows that the low conditional correlations of dollar-backed stablecoins with cryptocurrency portfolios make them particularly suitable as a hedge for crypto investors. It also shows that all stablecoins considered have high diversification capacities by systematically reducing portfolio tail risk.
Recent years have witnessed the rapid development of bitcoin as the first digital currency. Considering the advantages of bitcoin for both individuals and society, the price prediction of bitcoin is a hot topic. However, there remain two main challenges to be addressed in this task. Firstly, because bitcoin is vulnerable to the attitudes of the investors, incorporating the sentiment semantics from the social media into the prediction is challenging. Secondly, it is intractable to predict extreme volatility of the bitcoin price. To tackle the above challenges, this paper proposes to incorporate sentiment and temporal information simultaneously. For the first challenge, this paper employs external unsupervised corpus to conduct the domain-specific post-pretraining on the off-the-shelf language model. And the sentiment analysis on the tweets is done to obtain the scores. For the second one, Long Short Term Memory (LSTM) network is leveraged to joint model the temporal price data and the sentiment scores, thus deriving the final predictions. Experiments on the real data show that compared with the single-layer LSTM model, the model in this paper works better, which provides help for investors to specify trading strategies and also provides implications for government agencies that are developing digital currencies.
Bitcoin is the most famous digital currency in the world and has become an investment asset. Prediction is one of the important matters in the investment market. In the economic field, there are different studies on the reasons for the price change of Bitcoin and how to predict the price trend of Bitcoin or how Bitcoin studies the market. Therefore, for Bitcoin, predicting the trend of Bitcoin price can effectively help Bitcoin investors. Data from www. Coingecko, the price of bitcoin is sorted according to the time sequence. Using the time series model, the change of bitcoin price in a specific period which is from 28 April 2013 to 22 August 2022 is calculated to predict the future trend of bitcoin price. Data preprocessing includes attributes removal, stationary test, and differencing. In predicting the price of Bitcoin, the ARIMA method that can produce high accuracy in short-term prediction is adopted. Use prediction test AIC and Check the residuals to select the best prediction model among the candidate models. The results of model testing show that AIC of ARIMA (5,1,2) is the smallest among all candidate models, and the results of residual check also show that ARIMA (5,1,2) model is the best model for predicting four periods.
In this paper, Autoregressive Integrated Moving Average (ARIMA) model is used for analysing and forecasting the adjusted closing price of bitcoin. The whole dataset used is daily bitcoin closing price dating from Jan 2017 to Sep 2022. However, for testing the performance of the ARIMA model, the dataset is divided into two parts: the ARIMA model is built on training set and later using the test set to check the accuracy of the prediction. Two models, namely ARIMA (5, 2, 1) and ARIMA (0, 2, 2) are selected and comparations between them are made. ARIMA (5, 2, 1) is chosen with stepwise selection and approximated information criteria while ARIMA (0, 2, 2) is without stepwise selection and the information criteria is not approximated. Both pass the residual test and ARIMA (0, 2, 2) is slightly better according to AIC, AICc and BIC. Later, the predicting accuracy of the two models for different forecasting periods (5-day, 10-day, and long-term forecasting) are compared. It is not surprising that ARIMA performs better while making short term prediction. The 5-day and 10-day forecast works well while the long-term forecast is of limited practical value.
Given the skyrocketing returns earned by bitcoin, it has received widespread attention as an investment asset. The shocks experienced by stock and bond markets over time and especially during the COVID-19 pandemic has led to an evaluation of bitcoin as a wealth protection asset, a role that gold has played until now. The current paper tests the hedging and safe haven properties of bitcoin in a broad portfolio of both developed and emerging markets stocks, bonds and real estate over a period of 10 years and during COVID-19 pandemic. Using a DCC-GARCH method, the study finds weak hedge and safe haven benefits of bitcoin. The results of the study establish that there is still a long way to go before bitcoin displays a strong safe haven behavior. However, there is a need for portfolio managers to become more cognizant about bitcoin given its potential to protect their portfolios.
The time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose challenges, given the difficulty in quantifying and modeling Bitcoin’s price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities of deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) algorithms with single, double, and triple layer network architectures to forecast Bitcoin’s realized price volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility.
With the rise of Blockchain technology, the cryptocurrency market has been gaining significant interest. In particular, the number of cryptocurrency traders and the market capitalization have grown tremendously. However, predicting cryptocurrency price is very challenging and difficult due to the high price volatility. In this paper, we propose a classification machine learning approach in order to predict the direction of the market (i.e., if the market is going up or down). We identify key features such as Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) to feed the machine learning model. We illustrate our approach through the analysis of Bitcoin close price. We evaluate the proposed approach via different simulations. Particularly, we provide a backtesting strategy. The evaluation results show that the proposed machine learning approach provides buy and sell signals with more than 86% accuracy.
This research provides the very first empirical investigation of the impact of the Russia-Ukraine war on the cryptocurrency market (Bitcoin trading volume, and returns). The findings indicate that the Russia-Ukraine war impedes Bitcoin trading volume. A 1% increase in the Russia-Ukraine war leads to a 0.2% reduction in Bitcoin trading volume. The findings also indicate that the impact is more pronounced during the post-invasion period, especially after one week of the invasion. Finally, the Russia-Ukraine war predicts Bitcoin returns in both the short and long run.
Open access
Environmental and Biological Research in Conflict Zones
Aymen TURKI, Ahmed OBEID, Sahar Loukil, Ahmed Jeribi
This study examines the connectedness between G7 indices, Bitcoin, and oil during the COVID-19 pandemic. Based on daily data from January 1, 2016 to April 1, 2021, a vector auto-regression model and an impulse response function are employed to illustrate the time path of these assets following own and cross-shocks. Our study exhibits the considerable effect of the pandemic on increasing directional causalities and time-varying connectedness between G7 indices, Bitcoin, and oil. The findings indicate that G7 indices’ own shocks almost immediately lower forecasts of stock return urging the diversification to reduce risk. Moreover, the significant negative response of oil to shocks amid the pandemic reflects its high vulnerability during mitigated periods. Unlike other countries, we find a relative resilience of Bitcoin to S&P 500 shocks, and we consequently recommend Bitcoin as a diversifier to Americaninvestors during the pandemic. Our results are useful for both investors and policymakers who need to think ahead, rather than waiting to have a downside G7 returns movement in turbulent periods.
This paper extends the study of Bourghelle et al. (2022) to check whether collective emotions could help to forecast bitcoin volatility over the period 2018-2021. To this end, we first assess whether consideration of investor sentiment and collective emotions can give us clearer insights into bitcoin dynamics over the period in question and whether they can help to explain the different price fluctuations. Formally, we ran causality tests and, as in Bourghelle et al. (2022), built a two-equation nonlinear vector autoregressive (VAR) model to assess for further lead-lag effects between bitcoin volatility and collective emotions. Second, we proposed in-sample forecasts of bitcoin volatility to test whether our forecasts could be improved by taking investors’emotions and sentiment into account. Our findings show that market sentiment and investors’ emotions provide useful information that can help to explainfluctuations, structural breaks, and changes in bitcoin volatility. Further, collective emotions improve bitcoin volatility forecasting as our nonlinear model, including emotions-related news, supplants the benchmark linear model.
Currently, the most popular cryptocurrency is bitcoin. Predicting the future value of bitcoin can help investors to make more educated decisions and to provide authorities with a point of reference for evaluating cryptocurrency. The novelty of the proposed prediction models lies in the use of artificial intelligence to identify movement cryptocurrency prices, particularly bitcoin prices. A forecasting model that can accurately and reliably predict the market’s volatility and price variations is necessary for portfolio management and optimization in this continually expanding financial market. In this paper, we investigate a time series analysis that makes use of deep learning to investigate volatility and provide an explanation for this behavior. Our findings have managerial ramifications, such as the potential for developing a product for investors. This can help to expand upon our model by adjusting various hyperparameters to produce a more accurate model for predicting the price of cryptocurrencies. Another possible managerial implication of our findings is the potential for developing a product for investors, as it can predict the price of cryptocurrencies more accurately. The proposed models were evaluated by collecting historical bitcoin prices from 1 January 2021 to 16 June 2022. The results analysis of the GRU and MLP models revealed that the MLP model achieved highly efficient regression, at R = 99.15% during the training phase and R = 98.90% during the testing phase. These findings have the potential to significantly influence the appropriateness of asset pricing, considering the uncertainties caused by digital currencies. In addition, these findings provide instruments that contribute to establishing stability in cryptocurrency markets. By assisting asset assessments of cryptocurrencies, such as bitcoin, our models deliver high and steady success outcomes over a future prediction horizon. In general, the models described in this article offer approximately accurate estimations of the real value of the bitcoin market. Because the models enable users to assess the timing of bitcoin sales and purchases more accurately, they have the potential to influence the economy significantly when put to use by investors and traders.
Nosipho Mthembu, Kazeem Abimbola Sanusi, Joel Hinaunye Eita
The study investigates the effects of stock market volatility and cybercrime on cryptocurrency returns in the South African economy. Daily time series data on four different types of cryptocurrencies (Bitcoin, Ethereum, Tether, and BMB) were employed. The data covers the period from 1 January 2019–31 December 2021. The study employed the dynamic conditional correlation (DCC GARCH) and Bayesian liner regression model to investigate time-varying correlations among the variables. Empirical findings suggest that stock market volatility has a positive impact on the returns of BNB, Bitcoin, and Ethereum. However, it has a negative impact on Tether. Expectedly, cybercrime poses negative impacts on the returns of BNB, Bitcoin, and Ethereum but could be said to have no impact on the returns of Tether. The study concludes that ongoing efforts to reduce cybercrime activities need to be strengthened to further the use of digital currencies.
Wajid Shakeel Ahmed, Ahsan Mehmood, Talha Sheikh, Allah Bachaya
This paper investigated the relationship between cryptocurrencies and emerging stock market indices using fractional integration and co-integration technique. Particularly, fractional integration is applied to examine stochastic properties of individual assets and fractional cointegration to analyse bivariate connectedness. Our findings unveil the absence of mean reversion in majority cases which indicates high persistence in series. Furthermore, bivariate analysis reveals disconnection between cryptocurrencies prices and stock indices. Surprisingly, a different picture emerges on using conditional volatility instead of prices. Like, conditional volatility-based estimation uncovers evidence of mean reversion in univariate analysis as expected. There is some evidence of cointegration on volatility grounds between cryptocurrencies and emerging stock market indices. Our findings implies that investment decision regarding digital currencies should be taken cautiously. As cryptocurrencies are extremely volatile with high degree of persistence which can make them counterproductive.
The research and investment community seems to ignore the long-term sustainability of Bitcoin, which is reflected in four flaws: transaction fees, miners' revenue, concentration and electricity consumption. While most of the authors have aimed to examine one topic at a time, with a particular interest in electricity consumption and carbon footprint, the aim of this paper is to examine all these issues simultaneously to provide a more comprehensive view on long-term sustainability of Bitcoin. This paper looks at these flaws and reveals why Bitcoin is not sustainable in the long run, how decentralization is being lost, how the design is putting artificial and unrealistic pressure on the ecosystem, while all being powered by an unjustifiable amount of dirty electricity sources. Our main findings are as follows. Firstly, transaction fees are already high and set to increase in time, further discriminating small transactions against big ones. Secondly, miners' revenue comes mostly from the block reward. The block reward is the main income source for miners, but is set to be cut on a regular basis, making miners' revenue not sustainable in the long run. Thirdly, miner concentration is already an issue, with a possibility of deepening even more and diminishing the idea of decentralization. Fourthly, the high electricity demand and the associated carbon footprint thus cannot be justified by any means. We deem our results useful for overall policy and regulatory implications.
The global economy receives a catastrophic blow due to the COVID-19 epidemic, with long-term pessimism shown towards the global market and exponential increasing expectation for looking for a reliable safe-haven asset. Now, it seems the possible alternatives to traditional currencies issued and backed by governments have primarily emerged - in the form of Bitcoin and Ethereum, as well as several other cryptocurrencies. Cryptocurrencies have been on the market for a long time and have been controversial in the international financial markets for their unique properties. Since the outbreak of COVID-19, the price of cryptocurrencies has seen an unprecedented increase. Whether the price increase of cryptocurrencies is linked to the COVID-19 outbreak is a mystery. This paper will focus on exploring this question through a linear regression machine learning model. The data in the U.S.A are used here. Our results show that the price of bitcoin is significantly related to the price of Ethereum. There is some correlation between covid-19 new cases incensement and the price of Bitcoin and Ethereum, indicating the legitimacy of predicting cryptocurrencies’ price using covid-19 new cases incensement as a factor.