Bitcoin is a digital asset that was first mined in January 2009 after the global financial crisis of 2007–2008. Over a decade later, there is still no consensus across different market regulations on the classification, use cases, policies, and economic implications of bitcoin. However, there is an increasing demand for digital currency, as an alternative to fiat currency which would spur financial innovation and inclusion. This study reviews regulations on digital assets across countries. It further discusses some use cases for bitcoin to reduce financial risk and facilitate cross border transactions. The study also discusses challenges related to bitcoin such as: cryptocurrencies substitution, cross border financing, cyber risk and security, and benefits in terms of the effect of coronavirus on the speed of capital market innovation and hence bitcoin usage. The study concludes by examining the economic effect of bitcoin halving events on the U.S. capital market to better understand the influence of bitcoin on financial markets and key drivers of its intrinsic value. The empirical evidence from this study suggests that bitcoin halving events are associated with significant negative stock market reaction, signaling a trading tradeoff between cryptocurrencies and U.S. stock markets.
The objective of this study is to examine the movement of Bitcoin and the traditional currencies (USD, EURO, GBP and CNY) and the Bitcoin’s hedging of the traditional currencies. First, this paper observes the Bitcoin and four traditional currency exchange series: the USD, EURO, GBP and CNY. Second, it examines the fluctuation patterns of each series by using wavelet transform analysis, Third, a wavelet coherence analysis is applied to examine the interdependence between the Bitcoin and the four traditional currencies. The phase pattern analysis results indicate that the Bitcoin may not act as a hedging currency to replace the traditional currencies during the Covid-19 crisis. Another interesting result shows the rapid increasing number of the World Covid-19 Deaths (CovidDeaths) may not be the critical reason for the hyper price of the Bitcoin. The massive quantitative easing (QE) may be considered as the key reason for the soar-up of the Bitcoin price.
The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using machine learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a machine learning approach to predict the direction of the mid-price changes on the upcoming tick. We show that there are universal features amongst cryptocurrencies which lead to models outperforming asset-specific ones. We also show that there is little point in feeding machine learning models with long sequences of data points; predictions do not improve. Furthermore, we solve the technical challenge to design a lean predictor, which performs well on live data downloaded from crypto exchanges. A novel retraining method is defined and adopted towards this end. Finally, the trade-off between model accuracy and frequency of training is analyzed in the context of multi-label prediction. Overall, we demonstrate that promising results are possible for cryptocurrencies on live data, by achieving a consistent 78% accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs. US dollars.
Samuel Asumadu Sarkodie, Maruf Yakubu Ahmed, Phebe Asantewaa Owusu
The COVID-19 global pandemic has disrupted business-as-usual, hence, affecting sustained economic development across countries. However, it appears economic uncertainty following COVID-19 containment measures favor market signals of cryptocurrencies. Here, this study empirically and structurally investigates the implication of COVID-19 health outcomes on market prices of Bitcoin, Bitcoin Cash, Ethereum, and Litecoin. Evidence from the novel Romano-Wolf multiple hypotheses reveal COVID-19 shocks spur Litecoin by 3.20-3.84%, Bitcoin by 2.71-3.27%, Ethereum by 1.43-1.75%, and Bitcoin Cash by 1.34-1.62%.
Virtual currency represents a specific technological innovation on financial markets. Bitcoin and other cryptocurrencies are popular alternatives to traditional cash and investment. We indicate a research gap in the literature review. We find out that current research focused rarely on portfolio diversification using bibliographic analysis in VOSviewer. We think that portfolio diversification is extremely important on the crypto market for most investors because virtual currencies are very risky compared to traditional assets. The primary aim is to construct an optimal portfolio consisting of several cryptocurrencies without traditional assets using a modern theory portfolio. The total sample consists of 16 virtual currencies from 1 October 2017 to 13 January 2020. We mainly obtain historical data on the daily close price of cryptocurrencies from Yahoo Finance. The results show that the optimal portfolio using Markowitz approach consists of Cardano, Binance Coin, and Bitcoin. In addition, virtual currencies are moderately Correlated, with the exception of Tether based on correlation analysis. The high correlation is dangerous for cryptocurrency in portfolio diversification. However, Tether is an atypical virtual currency compared to other cryptocurrencies.
The study investigates and develops the ability of the extreme value theory (EVT) to predict bitcoin return. EVT is used to deal with rare but extreme events, such as severe losses or excessive damages. It is being used as a powerful statistical tool in various disciplines, including finance, engineering, environmental science, and actuarial science. As the largest among all cryptocurrencies in existence, bitcoin’s behavior is primarily characterized by great volatility. Predicting bitcoin return is complex and important, primarily because of the extreme nature of its return. There is not enough substantial research involving EVT in bitcoin analysis. This study has three objectives. First, confirming the extreme nature of bitcoin return by various statistical tests; second, modeling the bitcoin return using two different EVT approaches (block maxima approach and peak over threshold approach); and third, assessing uncertainties by predicting bitcoin return levels for 5-, 10-, 20-, 50-, and 100-years with a 95% confidence interval using both of these methods. These results could certainly serve policymakers and investors, as these return levels can be useful in characterizing bearish and bullish trends and predicting the same. Moreover, these can serve as starting points for future studies regarding the stationary and non-stationary properties of bitcoin return.
The cryptocurrency price movement behaves randomly and fluctuates like other stock markets. Prediction of cryptocurrency is a recent area of research interest and budding fast. The underlying nonlinearities in its price series make its prediction challenging. Sophisticated methodologies for accurate prediction of cryptocurrency are highly desired. Artificial neural networks (ANNs) are good approximators, however their accuracy is greatly subjective to optimal network structure and learning method. This article designs optimal ANNs for efficient cryptocurrency prediction using quasi opposition based Rao algorithms, i.e. QORA-ANN. The model explores a set of potential ANNs in the search space and lands at an optimal network through the evolving process. Historical data from four emerging cryptocurrencies such as Bitcoin, Litecoin, Ethereum, and Ripple are used to evaluate the QORA-ANN. The prediction ability of the proposed approach is compared with few similar methods such as ANN trained with genetic algorithm, differential evolution and particle swarm optimization (i.e. ANN-GA, ANN-DE, ANN-PSO), support vector machine (SVM), and multilayer perceptron (MLP). From exhaustive simulation studies and comparative result analysis it is found that the QORA-ANN method performed better than others and hence can be suggested as an efficient tool for cryptocurrencies prediction.
Κωνσταντίνος Γκίλλας, Rangan Gupta, Christian Pierdzioch
We use intraday data to construct measures of the realized volatility of bitcoin returns. We then construct measures that focus exclusively on relatively large realizations of returns to assess the tail shape of the return distribution, and use the heterogeneous autoregressive realized volatility (HAR-RV) model to study whether these measures help to forecast subsequent realized volatility. We find that mainly forecasters suffering a higher loss in case of an underprediction of realized volatility (than in case of an overprediction of the same absolute size) benefit from using the tail measures as predictors of realized volatility, especially at a short and intermediate forecast horizon. This result is robust controlling for jumps and realized skewness and kurtosis, and it also applies to downside (bad) and upside (good) realized volatility.
This article explores the Bitcoin return predictability of variables constructed from one-minute high-frequency Bitcoin trading data. During the training period of 2012–2018, LASSO is used to pick out the most powerful predictors. We then use predictors selected by LASSO to predict the Bitcoin returns in the 2018–2019 test sample. An investment strategy based on the return predictions outperforms a simple buy-and-hold strategy and other strategies based on the prediction of Ordinary Least Squares and Neural Networks.
This paper investigates the prediction power of economic policy uncertainty on Bitcoin trading (return, volume, and volatility) over the period from May 2013 to June 2019. We employ the Transfer Entropy model with the following two different regimes (i) stationary and (ii) nonstationary assumption. We construct different algorithm calculations for returns, volume and volatility to test how this proxy impacts. We find that the global Economic Policy Uncertainty negatively causes Bitcoin volumes and volatilities. Therefore, under uncertain regimes, investors are risk-averse to trade, which makes the market less volatile. Our findings confirm the existence of pessimistic risk premium, the theory of deteriorating liquidity and the widen bid-ask spread, which lead to a decline in trading volume under uncertainties in the Bitcoin market. By using different reliable data sources as well as expanding timeframe until May 2020 with COVID-19 pandemic, our results remain robust. Hence, the practical implications will be the useful tools for different parties in the Bitcoin market in the financial turbulence context.
Amir Abbaszadeh Sori, Mehdi Golsorkhtabaramiri, Ali Abbaszadeh Sori
In the last few years, cryptocurrencies have found a special place in the free economy. In addition to the importance and economic features of cryptocurrencies, the technical perspective on this area is also significant. If we want to use cryptocurrencies in the future as a global technology with everyday use, then this field needs to be optimized. In addition to issues such as security, scalability, speed, etc., energy efficiency and sustainability should also be considered. In this paper, the proposed “green efficiency” characteristic is added to the quality model for the field of cryptocurrency. This characteristic consists of four units that have independent tasks, the overall process of which seeks to design an optimal quality model in terms of energy consumption in cryptocurrency. The central unit of this proposal is the G-ECC, which controls other units. The unit makes the final decision to reduce energy consumption and trade-offs between features by reviewing and evaluating reports received from other units. At the end of this article, the green efficiency of IOTA cryptocurrency is reviewed in the proposed model.
Purpose After the COVID-19 outbreak, the Federal Reserve has undertaken several monetary policies to alleviate the pandemic consequences on the markets. This paper aims to evaluate the effects of the Federal Reserve monetary policy on the cryptocurrency dynamics during the COVID19 pandemic. Design/methodology/approach We examine the response and feedback effects via an event study methodology. For this purpose, abnormal returns (AR) and cumulative abnormal returns (CARs) around the first FOMC (Federal Open Market Committee) announcement related to the COVID-19 pandemic for the top five cryptocurrencies are explored. We, further investigate the effect of the eight FOMC statement announcements during the COVID19 pandemic on these cryptocurrencies (Bitcoin, Ethereum, Tether, Litecoin, and Ripple). In the above-mentioned crypto-currency markets, we investigate the presence of bubbles by using the PSY test. We then examine the concordance of the dates of these bubbles with the dates of the FOMC announcements. Findings The empirical results show that the first FOMC event has a negative significant effect after 4 days of the announcement date for all studied cryptocurrencies except Tether. The results also indicate that cumulative abnormal returns are significant during the event windows of (−3,8), (−3,9), and (−3,10). Besides, we find that Bitcoin, Ethereum and, Litecoin lived short bubbles lasting for a few days. However, Ripple and Tether markets present no bubbles and no explosive periods. Research limitations/implications This paper presents trained proof that FOMC announcements have a positive effect on volatility's predictive capacity. This work therefore promotes the study of the data quality of volatility in future research as well. Practical implications The justified effect of the FOMC announcements on cryptocurrency as a speculative asset has practical implications for investors in building their trading strategies in anticipation of the next FOMC announcement. Therefore, this study implies that the FOMC announcements contain very relevant information for investors in the cryptocurrency market. This research may not only encourage a better understanding of the evolution of the expectations of policymakers, but also facilitate a better understanding of how these expectations are developed. Originality/value The COVID-19 pandemic has disturbed the stability of financial markets, inciting the Fed to take some monetary regulations. To the best of our knowledge, this study is the first one that analyses the response of five major cryptocurrencies to FOMC announcements during COVID 19 pandemic and associates these dates with bubble occurrences.
With its rising popularity, the Bitcoin has also become increasingly independent from global financial markets. Recently, it has joined the class of alternative assets. We use the newly developed wavelet methodology to analyze daily data to compare the COVID-19 pandemic at the beginning of 2020 with the bear market episode at the end of 2018. In both cases, attention signals and a general panic are the main drivers of the Bitcoin fluctuations. We show that the Bitcoin’s dynamic is more complex than the dynamics of standard financial assets. The Bitcoin is, on the one hand, subject to pandemic shocks but also represents an important source of attention signals. On the other hand, because the Bitcoin additionally reacts on an emotional basis, it might react faster than other assets and thus creates a market signal itself. Moreover, we identify short cycles (of several days), which may possibly be related to demand factors, while long cycles (of several weeks) seem to mirror supply factors and might be related to Bitcoin mining in China. Finally, the analysis underlines the importance of continuous financial education and communication by the supervisory authorities about new, alternative financial assets.
Khaled Mokni, Elie Bouri, Ahdi Noomen Ajmi, Xuan Vinh Vo
This paper examines the hedge and safe-haven abilities of Bitcoin against U.S. aggregate and categorical economic policy uncertainty (EPU) via the application of quantile regression model augmented with a dummy and some control variables. Using monthly data from September 2011 to December 2019, empirical results indicate that Bitcoin does not act as a strong hedge against the aggregate U.S. EPU. However, it acts as a strong safe-haven for this aggregate measure of uncertainty when the Bitcoin market is bearish. Looking deeper into the disaggregated level of the U.S. EPU data, the analyses involving categorical EPU data indicate the ability of Bitcoin to act as a strong hedge and safe-haven against specific uncertainties related to fiscal policy, taxes, national security, and trade policy.
Ahmed Jeribi, Yasmine Snene Manzli, Islem Khefacha
Abstract Using the DCC-GARCH (1.1) model, we investigate the dynamic conditional correlations between Tunisian indices, digital assets, and gold prices for the period ranging from 4 January 2016 to 30 April 2020. Our findings reveal that digital assets (Bitcoin, Ripple, Ethereum, and Dash) and gold can be considered as hedge and diversifier assets before the 2020 global pandemic. Contrarily to Ripple which can be a safe haven asset for the Tunisian investors in early 2020, Monero can be considered as a diversifier asset more than a hedge. Finally, our results can be useful to Tunisian investors when accounting for implementing hedging strategies. JEL classification: C22, C5, G1
Bitcoin, 2009 yılında ortaya çıkmasıyla devrim niteliğinde bir altyapı sunan kripto para türüdür. Bitcoinin temel teknolojisi olan blokzincir, güvenilir bir üçüncü tarafa ihtiyaç duymayan, merkezi olmayan bir sistem olarak tasarlanmış; geniş uygulama potansiyeli ile kamu ve iş dünyasında hızla kabul görmüştür. Bu çalışmada, Ocak 2012 – Mart 2020 tarihleri arasında cumhuriyet altını, altın ons fiyatı, ham petrol fiyatı, amerikan doları ve euro para birimleri ile bitcoin arasındaki korelasyon ilişkisi incelenmiştir. Araştırma kapsamında Spearman korelasyon analizinden faydalanılmıştır. Analizlerin sonucuna göre, piyasalara girdiği ilk dönem olan 2012’de bitcoinin diğer göstergelerle arasında istatistiksel olarak anlamlı bir ilişkisi saptanmamıştır (p>0.05). Bitcoinin dolar karşılığının bir önceki yıla göre üç katından fazla arttığı 2017 yılı ise bitcoinin zirve yılı olup; euro ile arasında pozitif yönlü kuvvetli bir ilişki vardır (r=0.873; p
Ama -almada nemli fiyat dalgalanmalarna sahip kripto paralardan en yksek ilem hacmine sahip olan Bitcoin'in volatilite dinamiklerini tespit etmek iin Bitcoin getirilerinin ykseli/kazandran ve d/kaybettiren rejimleri, rejim gei olaslklar ve rejimde kalma srelerinin tespit
The high volatility of an asset in financial markets is commonly seen as a negative factor. However short-term trades may entail high profits if traders open and close the correct positions. The high volatility of cryptocurrencies, and in particular of Bitcoin, is what made cryptocurrency trading so profitable in these last years. The main goal of this work is to compare several frameworks each other to predict the daily closing Bitcoin price, investigating those that provide the best performance, after a rigorous model selection by the so-called k-fold cross validation method. We evaluated the performance of one stage frameworks, based only on one machine learning technique, such as the Bayesian Neural Network, the Feed Forward and the Long Short Term Memory Neural Networks, and that of two stages frameworks formed by the neural networks just mentioned in cascade to Support Vector Regression. Results highlight higher performance of the two stages frameworks with respect to the correspondent one stage frameworks, but for the Bayesian Neural Network. The one stage framework based on Bayesian Neural Network has the highest performance and the order of magnitude of the mean absolute percentage error computed on the predicted price by this framework is in agreement with those reported in recent literature works.
The emerging cryptocurrency market has lately received great attention for asset allocation due to its decentralization uniqueness. However, its volatility and brand new trading mode have made it challenging to devising an acceptable automatically-generating strategy. This study proposes a framework for automatic high-frequency bitcoin transactions based on a deep reinforcement learning algorithm-proximal policy optimization (PPO). The framework creatively regards the transaction process as actions, returns as awards and prices as states to align with the idea of reinforcement learning. It compares advanced machine learning-based models for static price predictions including support vector machine (SVM), multi-layer perceptron (MLP), long short-term memory (LSTM), temporal convolutional network (TCN), and Transformer by applying them to the real-time bitcoin price and the experimental results demonstrate that LSTM outperforms. Then an automatically-generating transaction strategy is constructed building on PPO with LSTM as the basis to construct the policy. Extensive empirical studies validate that the proposed method performs superiorly to various common trading strategy benchmarks for a single financial product. The approach is able to trade bitcoins in a simulated environment with synchronous data and obtains a 31.67% more return than that of the best benchmark, improving the benchmark by 12.75%. The proposed framework can earn excess returns through both the period of volatility and surge, which opens the door to research on building a single cryptocurrency trading strategy based on deep learning. Visualizations of trading the process show how the model handles high-frequency transactions to provide inspiration and demonstrate that it can be expanded to other financial products.