Blockchain Papers

Yonghong Jiang, Jiayi Lie, Jieru Wang, Jinqi Mu

No abstract is available for this record.

Myeong Jun Kim, Canh Phuc Nguyen, Sung Y. Park

No abstract is available for this record.

Xianfang Su, Yong Li

This paper examines the sentiment spillovers among oil, gold, and Bitcoin markets by employing spillovers index methods in a time-frequency framework. We find that the total sentiment spillover among crude oil, gold and Bitcoin markets is time-varying and is greatly affected by major market events. The directional sentiment spillovers are also time-varying. On average, the Bitcoin market is the major transmitter of directional sentiment spillovers, whereas the crude oil and gold markets are the major receivers. In particular, the sentiment spillover effects are major created at high-frequency components, implying that the markets rapidly process the sentiment spillover effects and the shock is transmitted over the short-term. Moreover, we also find that the sentiment spillover effects differ significantly in term of intensity and direction when compared with return and volatility spillover effects. The present study has certain applications for investors and policymakers.

Libo Yin, Jing Nie, Liyan Han

No abstract is available for this record.

Maurice Omane‐Adjepong, Paul Alagidede

No abstract is available for this record.

Mourad Mroua, Slah Bahloul, Nader Naifar

This paper investigates the potential portfolio diversification benefits by introducing the Bitcoin to the traditional diversified financial portfolio. Using a bootstrap-based stochastic dominance (SD) test and daily prices of the commodity and stock market indices, we find that the introduction of Bitcoin improves the performance of the traditional financial portfolio and the optimal portfolio choice changes according to the market regime. Principally, results show that the optimal portfolio diversification combining Bitcoin, US stock market and commodities indices can be a good hedge, offering risk-averse, more performing portfolio investments during any financial crisis.

Takahiro Hattori, Ryo Ishida

Abstract We examine how investors arbitrage the Bitcoin spot and futures markets. Using intraday data of the Chicago Board Options Exchange, we reconstruct the actual arbitrage condition that investors confront. We find that there are few arbitrage profit opportunities in “normal” markets, but large arbitrage profit opportunities arise during Bitcoin market “crashes.”

Alexey Mikhaylov

The paper focuses on the analysis of the cryptocurrency open innovation market to predict sustainable growth in the future. The nature of cryptocurrencies ‘development leads to the rapid increase in their popularity and spread of trading at this new market. The high volatility of these assets is encouraging to understand and predict their price in ever changing market environment. The paper proposed the pool complexity approach to choose optimal technology using social activity in the internet, trading parameters, technical indicators and other cryptocurrency data. According to the results of the analysis, the most effective and promising cryptocurrency is EOS cryptocurrency, which has the lowest complexity and commission level among the analyzed digital currencies and allows you to implement third-party applications in the system.

Dimitrios Koutsoupakis

Purpose While monetary autonomy is self-explanatory for cryptocurrencies such as Bitcoin with predetermined supply path, it is of great interest to probe into the monetary structures of Stablecoins. In these supply contracts and expands and capital restrictions apply due to the existence of reserves as the exchange rate arrangement adheres to a price rule. Design/methodology/approach Ever since the launch of Bitcoin and its offspring, examination of cryptocurrencies' trading activity from the empirical finance viewpoint has received much attention and continues to do so. The particular monetary arrangements found in Stable cryptocurrencies (colloquially referred to as Stablecoins), however, have not been properly (1) classified and (2) studied within an empirical international finance and banking context. This paper provides an empirical framework analogous to Impossible Trinity for exploring monetary arrangements across Stablecoins wherein reserves are held as price stability is targeted. Findings The study findings of existence of the degree of achievement along the three dimensions of the Impossible Trinity hypothesis, namely monetary independence, exchange rate stability and financial openness for a representative sample able to cover all varieties of Stablecoins, provide fresh empirical insights and arguments to this growing literature with respect to the success of their embedded exchange rate stabilization mechanisms. While the hypothesis can be supported for all cryptocurrencies in question, the trade-off combination among exchange rate stability, capital openness and monetary independence varies with the categorical types of Stablecoins. Research limitations/implications If Stable cryptocurrencies, therefore, claim the role of global monetary assets freed from sovereign limits and national boundaries, it is critical to explore whether they adhere to traditional monetary frameworks. It goes without saying that in this work the author does not use a complete catalogue of all the available Stablecoins, rather a complete catalogue of all the possible asset classes of Stablecoins. While there is a significant difficulty in finding Algorithmic Stablecoins and, so far, there is plethora of Stable Token initiatives, a broader sample to further examine these under this paper's empirical framework is suggested. Enrichment of the robustness analysis by constructing additional proxies, possibly building time series for the proposed cmo1 subindex and using additional estimation methods is encouraged. Practical implications Stablecoins have been developed aiming to address the issue of excessive price variation in cryptocurrencies such as Bitcoin. Holders of Stablecoins enjoy the combined advantages of using a blockchain-based digital infrastructure in fulfilling the functions of store of value and media of exchange and of using a traditional currency, which merely plays the role of the unit of account (and in some circumstances the trusted reserve to which is convertible to). Understanding the varieties of Stablecoins and quantifying the components for success of their price stabilization may result in designing better Stablecoins. Social implications Blockchain and cryptocurrencies have introduced new challenges to money and banking. Cryptocurrencies, which independently float such as Bitcoin, have gained the interest so far due to price variation that allows for gains. But these should be by far not considered to be a substitute to traditional means of payment. Lately, Stablecoins have increasingly gained attention for that USD Tether/Bitcoin pair (a Stablecoin pegged to the US dollar at parity) has outrun the US dollar/Bitcoin pair as the most traded pair in digital exchanges marking the strong position and high demand for Stablecoins. Originality/value This approach uncovers the varieties of Stablecoins with respect to their monetary constraints compared to the rest of the cryptocurrencies, which independently float. In this paper, the author provides a conceptual framework for the analysis of the exchange rate mechanisms conditional on Stablecoin asset classes accompanied with an empirical study from the monetary viewpoint. This is the first work in this attempt. The empirical framework employed is analogous to the traditional theory of international monetary economics referred to as Impossible Trinityz. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/JES-06-2020-0279

Agi Prasetiadi

COVID-19 affects significant human activity around the globe, including Bitcoin prices. The Bitcoin price is well known for its volatility, so it is not a big shocker when the panic-selling occurs during the pandemic. However, the mechanism to cope with these breakouts, especially the bearish one, is contentious. The experts give numerous pieces of advice with different conclusions in the end. It is also the same with Machine Learning. Various kernels show different results regarding how the price will move. It depends on the window size, how the data is being preprocessed, and the algorithm used. This paper inspects the best combination that various machine learning can offer with a linear approach to navigate the price prediction based on its depth interval, window size until the algorithms themselves. This paper also proposed a new approach to seeing the prediction range called s-steps ahead prediction using a linear model. The result shows that simple machine learning can herd 99.715% profit even during the bearish breakout.

Abdolhossein Zameni, Nafis Alam

No abstract is available for this record.

Ahmed Jeribi, Dhouha CHAMSA, Yasmine Snene Manzli

In this study we discuss the determinants of the BRICS and GCC stock market returns during the COVID-19 outbreak. We employ the OLS regression to discern how crypto-currencies, VIX, oil, GOLD prices, and the number of COVID-19 cases and deaths, affect the Gulf and BRICS stock markets. We find that Bitcoin and Ethereum can generate benefits from portfolio diversification and hedging strategies but not from safe haven strategies for Russia, Brazil, Abu-Dhabi, Bahrain, and Qatar financial investors during the COVID-19 outbreak. Our results reveal that Gold is neither hedge nor a safe haven but is only an effective diversifier for investors during the COVID-19 outbreak. The results indicated that among all the BRICS and GCC stock indexes, the expected volatility of the US stock market has an effect only on china and Kuwait financial markets. Finally, our results show that the growth rate of confirmed COVID-19 cases has a negative impact only on South Africa and Brazil stock market.

Mohammad Ali, Swakkhar Shatabda

Bitcoin is the most popular and valuable cryptocurrency in the financial market which attracts traders for investment and opens new research opportunities for researchers. Many research works have been done on bitcoin price prediction with different machine learning prediction algorithms. Researchers take relevant features from the dataset which have strong correalation with bitcoin prices and select random data chunks to train and test their model. Randomly selected data to train the model, may cause inappropriate results and reduce the accuracy of price prediction. In this paper, we investigate a proper data selection method to train a prediction model. We apply our proposed methodology to train a simple linear regression prediction algorithm. We predict bitcoin price for 7 days with the linear regression model. When we train the linear regression model with an appropriate data chunk identified by our methodologies, we find acceptable results for the prediction. The percentage error method is applied for error calculation which finds the accuracy is 96.97%. In the end of this manuscript, we conclude our work with future improvements.

Shuanglian Chen, Hao Dong

In this paper, we explore the volatility spillovers across different Bitcoin markets. We decompose the realized volatility into common and idiosyncratic volatilities, as well as the good and bad volatilities. Then the asymmetry in volatility spillovers between Bitcoin markets is measured by the DY (Diebold and Yilmaz) index. In addition, we construct statistics to test the asymmetry in volatility spillovers between different Bitcoin markets. The results are achieved as follows. The spillovers of systematic and idiosyncratic volatilities dominate the connectedness among different Bitcoin markets. In addition, the idiosyncratic volatility spillovers are more easily influenced by policies. Good volatility spillovers dominate the Bitcoin markets and change over time. The further results suggest that there is significant asymmetry between systematic and idiosyncratic volatility spillovers in the Bitcoin markets, while the asymmetries between good and bad volatility spillovers are heterogeneous in different markets. The findings in this paper can provide some suggestions for regulators controlling market stability and investors generating investment strategies.

Muhammad Abubakr Naeem, Elie Bouri, Zhe Peng, Syed Jawad Hussain Shahzad · 5 authors

No abstract is available for this record.

J.D. Agarwal, Manju Agarwal, Aman Agarwal, Yamini Agarwal

Artificial intelligence (AI) is becoming more dynamic and efficient for routine tasks than humans by the day, the question is will it replace humans in every sector. It is not true. Technology and human complement and do not compete with each other. Initially, it might create disruption in an existing ecosystem, later it helps in creating opportunities. Business must now embrace a new culture, where innovation and continuous learning are core components of the organizational culture. It sets the stage for agility, adaptability and growth. There are of course risks. AI and machine learning (ML) tools and techniques can be misused, intentionally or inadvertently. Obvious risk is misuse of AI by those intent on threatening individual’s physical, digital, financial, and emotional security. We have used worldwide real-life case scenarios to understand the importance of AI, its threats, and the role it plays in contributing toward the growth and prosperity of the society.

Moinak Maiti, Zoran Grubišić, Darko Vuković

The present study is on the five cryptocurrency daily mean return time series linearity dynamics during the Covid-19 period. These cryptocurrencies were chosen based on their influence on the market, primarily driven by its market capitalisation. Tether is included as the most important stable coin on the market, nominally pegged to the U.S. dollar (USD). The reason to investigate it is that there are some inconsistencies in its behaviour as opposed to the other four cryptocurrencies. This study found that the behaviour of Tether cryptocurrency daily average return time series pattern is highly nonlinear and chaotic in nature, whereas the other four cryptocurrencies (namely Bitcoin, Ethereum, XRP and Bitcoin Cash) daily average return time series were found to be linear in nature. To further study Tether’s nonlinear time series rich dynamics, this study deployed one category of the regime switching models popularly known as the threshold regressions. The study estimates fairly suggest that both the threshold autoregression (TAR) and smooth transition autoregressive (STAR) models with lag 1 are adequate to capture the rich nonlinear and chaotic dynamics of Tether’s daily average return time series.

Maurice Omane‐Adjepong, Paul Alagidede

This paper investigates asymmetry and local leverage behaviour in individual and aggregate markets of leading cryptocurrencies, and compares such characteristics to diverse traditional emerging asset classes. Generally different from the cryptocurrencies, the results show diffuse evidence of asymmetry and a significant presence of local leverage in the emerging markets. New findings indicate that mega‐size cryptocurrencies like Bitcoin and Ripple exhibit return‐volatility behaviour whereby volatility changes in their markets increase rather by a response to positive shocks than by a response to negative shocks. Akin to safe net assets, particularly gold, the inverse asymmetric reactions of the cryptocurrency markets position them distinctively from the existing emerging markets, suggestive that the digital assets stand to offer potentials beyond being diversifiers.

Rym Regaïeg, Wajdi Moussa, Nidhal Mgadmi

This article aims to analyse the hedging, diversifier and safe-haven properties of Bitcoin for the US Dollar Index (USDI). We explore the long-term relationship between USDI and Bitcoin by estimating a Markov-switching autoregressive (MS-AR) model with two regimes. Thus, the data used cover the period from 18 January 2010 to 30 June 2017 for both USDI and Bitcoin. The empirical findings based on the analysis of the MS-AR model report that investing in Bitcoin involves more benefits than USDI even if the economy is in a recession. However, by examining Bitcoin and USDI volatility, the research findings underline positive dependency between the two. Such results denote that Bitcoin does not act as a hedge, and not even as a safe haven, against USDI. We found that Bitcoin is merely a diversifier for USDI. Accordingly, the outcomes will help investors and portfolio risk managers to make more up-to-date investment analyses and decisions.

Ahmed Ibrahim, Rasha Kashef, Liam Corrigan

Many traders participate in activities known as "day-trading", trading Bitcoin against the dollar bill as the United States Dollar (USD) on very short timeframes to squeeze out profits from small market fluctuations. This paper aims to help traders decide how to best act by creating a model that can predict price movement's direction for the next 5-min time frame. Several machine-learning models have been tested for this Up/Down binary-classification problem. In this paper, we provide a comparison of the state-of-art strategies in predicting the movement direction for bitcoin, including Random Guessing and a Momentum-Based Strategy. The tested models include Autoregressive Integrated Moving Average (ARIMA), Prophet (by Facebook), Random Forest, Random Forest Lagged-Auto-Regression, and Multi-Layer Perceptron (MLP) Neural Networks. The MLP deep neural network has achieved the highest accuracy of 54% compared to other time-series prediction models. Also, in this paper, various data transformation and feature engineering have been applied in the comparison.

Muhammad Umar, Syed Kumail Abbas Rizvi, Bushra Naqvi

No abstract is available for this record.

Negar Maleki, Alireza Nikoubin, Masoud Rabbani, Yasser Zeinali

Cryptocurrencies, which the Bitcoin is the most remarkable one, have allured substantial awareness up to now, and they have encountered enormous instability in their price. While some studies utilize conventional statistical and econometric ways to uncover the driving variables of Bitcoin's prices, experimentation on the advancement of predicting models to be used as decision support tools in investment techniques is rare. There are many different predicting cryptocurrencies' price methods that cover various purposes, such as forecasting a one-step approach that can be done through time series analysis, neural networks, and machine learning algorithms. Sometimes realizing the trend of a coin in a long run period is needed. In this paper, some machine learning algorithms are applied to find the best ones that can forecast Bitcoin price based on three other famous coins. Second, a new methodology is developed to predict Bitcoin's worth, this is also done by considering different cryptocurrencies prices (Ethereum, Zcash, and Litecoin). The results demonstrated that Zcash has the best performance in forecasting Bitcoin's price without any data on Bitcoin's fluctuations price among these three cryptocurrencies.

Rahma Chemkha, Ahmed BenSaïda, Ahmed Ghorbel

No abstract is available for this record.

Hui Xiao, Yiguo Sun

This paper aims to enrich the understanding and modelling strategies for cryptocurrency markets by investigating major cryptocurrencies’ returns determinants and forecast their returns. To handle model uncertainty when modelling cryptocurrencies, we conduct model selection for an autoregressive distributed lag (ARDL) model using several popular penalized least squares estimators to explain the cryptocurrencies’ returns. We further introduce a novel model averaging approach or the shrinkage Mallows model averaging (SMMA) estimator for forecasting. First, we find that the returns for most cryptocurrencies are sensitive to volatilities from major financial markets. The returns are also prone to the changes in gold prices and the Forex market’s current and lagged information. Then, when forecasting cryptocurrencies’ returns, we further find that an ARDL(p,q) model estimated by the SMMA estimator outperforms the competing estimators and models out-of-sample.