Blockchain Papers

Eunho Koo, Geonwoo Kim

No abstract is available for this record.

Salma Tarchella, Abderrazak Dhaoui

No abstract is available for this record.

Zhenghui Li, Zhiming Ao, Bin Mo

We employ the quantile-coherency approach and causality-in-quantile method to revisit the roles of Bitcoin, U.S. dollar, crude oil and gold for USA, Chinese, UK, and Japanese stock markets. The main results show that the impact of global financial assets varies across different investment horizons and quantiles. We find that in most cases, the correlation between global financial assets and stock indexes is not significant or is weakly positive. From the perspective of investment horizons (frequency domain), the correlation in the short term is mostly manifested in Bitcoin, while in the medium and long term it is shifted to dollar assets. At the same time, the relationships are significantly higher in the medium and long term than in the short term. From the point of view of quantiles, it shows a weak positive correlation at the lower quantile. However, the correlation between the two is not significant at the median quantile. At the high quantiles, there is a weak negative linkage. According to the causality-in-quantiles approach results, in most cases global financial assets have different degrees of predictive capacity for the selected stock markets. Especially around the median quantile, the predictive ability was strongest.

Guangxi Cao, Wenhao Xie

No abstract is available for this record.

Mahmoud Qadan, David Y. Aharon, Ron Eichel

No abstract is available for this record.

Turgay Münyas, Feryat Atasoy

Purpose: This study aims to investigate the causal relationships between Bitcoin prices and developed and developing country stock markets. Design/methodology/approach: In the analysis part of the study, the causality test developed by Findings: As a result of the analysis, a two-way causality was found between BTC and DJI, among the developed country stock markets. On the other hand, there was a causality relationship from FCHI to BTC, while there was no causality from BTC to FCHI. There was a causality relationship from BTC to N225, while there was no causality from N225 to BTC. Finally, no causality relationship was found between DAX and BTC. Looking at the developing country stock markets, however, there was no causality relationship from BIST to BTC, there was a causality relationship from BTC to BIST. There was no causality relationship from BVSP to BTC, but there was a causality relationship from BTC to BVSP. There was no causality from MOEX to BTC, but there was a causality relationship from BTC to MOEX. There was no causality from BSE to BTC, but it was found that there was a causality relationship from BTC to BSE. As can be seen from the results, it is seen that Bitcoin prices are the cause of the stock markets of developing countries. It has been determined that Bitcoin historical values are effective on BIST, BOVESPA, MOEX Russia and BSE Sensex 30. The findings of the study were discussed in the results section. Originality/value: It is of great importance for investors to follow the developments in the stock market indices subject to research simultaneously with the Bitcoin prices. It is important that investors who will invest in these markets do not ignore the relationship between these markets in portfolio diversification.

Elie Bouri, Rangan Gupta, Chi Keung Marco Lau, David Roubaud

We study whether level of risk aversion can be used to predict Bitcoin returns using copulas and quantile-based models. We find evidence of predictability when the market return is at extreme quantiles. Further analyses show that the cross-quantilogram is similar when risk aversion is at the low or medium level for various quantiles of Bitcoin returns. The predictability is positive when the risk aversion is at very low level. However, predictability becomes negative when both the risk aversion and Bitcoin returns are very high, suggesting that when risk aversion and Bitcoin returns are at very high levels, Bitcoin is less likely to have large gains.

Toan Luu Duc Huynh, Rizwan Ahmed, Muhammad Ali Nasir, Muhammad Shahbaz · 5 authors

In the context of the debate on cryptocurrencies as the 'digital gold', this study explores the nexus between the Bitcoin and US oil returns by employing a rich set of parametric and non-parametric approaches. We examine the dependence structure of the US oil market and Bitcoin through Clayton copulas, normal copulas, and Gumbel copulas. Copulas help us to test the volatility of these dependence structures through left-tailed, right-tailed or normal distributions. We collected daily data from 5 February 2014 to 24 January 2019 on Bitcoin prices and oil prices. The data on bitcoin prices were extracted from coinmarketcap.com. The US oil prices were collected from the Federal Reserve Economic Data source. Maximum pseudo-likelihood estimation was applied to the dataset and showed that the US oil returns and Bitcoin are highly vulnerable to tail risks. The multiplier bootstrap-based goodness-of-fit test as well as Kendal plots also suggest left-tail dependence, and this adds to the robustness of the results. The stationary bootstrap test for the partial cross-quantilogram indicates which quantile in the left tail has a statistically significant relationship between Bitcoin and US oil returns. The study has crucial implications in terms of portfolio diversification using cryptocurrencies and oil-based hedging instruments.

Laura Levulytė, Alfreda Šapkauskienė

No abstract is available for this record.

Erdinc Akyildirim, Oğuzhan Çepni, Shaen Corbet, Gazi Salah Uddin

In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil.

Achraf Ghorbel, Ahmed Jeribi

No abstract is available for this record.

Ze Shen, Qing Wan, David J. Leatham

One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting of bitcoin volatility are crucial for bitcoin investors’ decision-making analysis and risk management. However, most previous studies of bitcoin volatility were founded on econometric models. Research on bitcoin volatility forecasting using machine learning algorithms is still sparse. In this study, both conventional econometric models and a machine learning model are used to forecast the bitcoin’s return volatility and Value at Risk. The objective of this study is to compare their out-of-sample performance in forecasting accuracy and risk management efficiency. The results demonstrate that the RNN outperforms GARCH and EWMA in average forecasting performance. However, it is less efficient in capturing the bitcoin market’s extreme events. Moreover, the RNN shows poor performance in Value at Risk forecasting, indicating that it could not work well as the econometric models in explaining extreme volatility. This study proposes an alternative method of bitcoin volatility analysis and provides more motivation for economic researchers to apply machine learning methods to the less volatile financial market conditions. Meanwhile, it also shows that the machine learning approaches are not always more advanced than econometric models, contrary to common belief.

Yu Ma, Zhiqian Luan

No abstract is available for this record.

Muhammad Abubakr Naeem, Saba Qureshi, Mobeen Ur Rehman, Faruk Balli

This study quantifies the spillover effects among seven cryptocurrencies to explore the spillover characteristics of seven cryptocurrencies, namely, Bitcoin, Ethereum, Ripple, Litecoin, Monero, Stellar, and NEM. The connectedness networks of returns are based on standard VAR and quantile VAR spillovers. In addition, the framework focuses on intact, pre-, and post-COVID-19 crisis sub-sample periods. Our results highlight that Bitcoin, Litecoin, and Ripple are the dominant transmitters to return spillover. The strongest interconnection is found for Bitcoin/Litecoin and Ripple/Sellar pair. Interestingly, Ethereum is the unvarying recipient in the system and is influenced by most of the cryptocurrencies. Further, NEM exhibits no connection with any of the cryptocurrency in the network acting as a potential diversifier. The quantile spillovers suggest increased intensity of connectedness at right and left tails. The sub-sample analysis confirms the low network integration across the cryptocurrencies during pre-COVID period. Finally, the post-COVID period indicates tangled clusters across the cryptocurrencies. The analysis provides contrasting results as obtained in the pre-analysis phase. Implications for investors and policymakers are highlighted in the study.

Hao Wang, Xiaoqian Wang, Siyuan Yin, Hao Ji

No abstract is available for this record.

Dennys Mallqui, Ricardo A. S. Fernandes

Cryptocurrencies are one of the most important financial and technological innovations of recent years. Currently, the interest in Bitcoin has grown, for traders and the general public. However, its high volatility represents a challenge in terms of prediction models for day-trade operations. In this way, recent studies have been proposed to predict the Bitcoin price direction for day-trade operations, but the maximum accuracy obtained was around 57.5%. In order to contribute and advance the state-of-the-art, this article experiences the impact of use Blockchain data, international economic indices, social trends information and technical indicators to overcome the predictions of the Bitcoin price direction. Thus, it is proposed a methodology based on data collection and processing, where weighted moving averages (for the Blockchain data, economic indices and social media trends) and technical indicators (considering the Bitcoin exchange rate data) were extracted/calculated from the original databases. The attributes were submitted to the Information Gain algorithm to select the most relevant ones. In the sequence, Support Vector Machines and Artificial Neural Networks models were used to predict the Bitcoin price direction for day-trade purposes. As a result, it was possible to obtain an average accuracy of 63.84% in a 1-year prediction period, overcoming other related studies.

Anantha Divakaruni, Peter Zimmerman

In April 2020, the US government sent economic impact payments (EIPs) directly to households, as part of its measures to address the COVID-19 pandemic. We characterize these stimulus checks as a wealth shock for households and examine their effect on retail trading in Bitcoin. We find a significant increase in Bitcoin buy trades for the modal EIP amount of $1,200. The rise in Bitcoin trading is highest among individuals without families and at exchanges catering to nonprofessional investors. We estimate that the EIP program has a significant but modest effect on the US dollar–Bitcoin trading pair, increasing trade volume by about 3.8 percent. Trades associated with the EIPs result in a slight rise in the price of Bitcoin of 7 basis points. Nonetheless, the increase in trading is small compared to the size of the stimulus check program, representing only 0.02 percent of all EIP dollars. We repeat our analysis for other countries with similar stimulus programs and find an increase in Bitcoin buy trades in these currencies. Our findings highlight how wealth shocks affect retail trading.

Thomas Conlon, Shaen Corbet, Richard McGee

This letter revisits the time-series relation between cryptocurrency prices and forward inflation expectations. Using wavelet time-scale techniques, a positive link between cryptocurrencies and forward inflation rates is identified, focused on a brief period surrounding the onset of the COVID-19 pandemic. This coincides with a rapid and synchronized decrease in cryptocurrency prices and forward inflation expectations, followed by a swift recovery to pre-crisis levels. Outside of the crisis period, we find no clear evidence of any inflation hedging capacity of Bitcoin or Ethereum during times of increasing forward inflation expectations.

Zdravka Aljinović, Branka Marasović, Tea Šestanović

This paper proposes the PROMETHEE II based multicriteria approach for cryptocurrency portfolio selection. Such an approach allows considering a number of variables important for cryptocurrencies rather than limiting them to the commonly employed return and risk. The proposed multiobjective decision making model gives the best cryptocurrency portfolio considering the daily return, standard deviation, value-at-risk, conditional value-at-risk, volume, market capitalization and attractiveness of nine cryptocurrencies from January 2017 to February 2020. The optimal portfolios are calculated at the first of each month by taking the previous 6 months of daily data for the calculations yielding with 32 optimal portfolios in 32 successive months. The out-of-sample performances of the proposed model are compared with five commonly used optimal portfolio models, i.e., naïve portfolio, two mean-variance models (in the middle and at the end of the efficient frontier), maximum Sharpe ratio and the middle of the mean-CVaR (conditional value-at-risk) efficient frontier, based on the average return, standard deviation and VaR (value-at-risk) of the returns in the next 30 days and the return in the next trading day for all portfolios on 32 dates. The proposed model wins against all other models according to all observed indicators, with the winnings spanning from 50% up to 94%, proving the benefits of employing more criteria and the appropriate multicriteria approach in the cryptocurrency portfolio selection process.

Shinji Kakinaka, Ken Umeno

This study investigates asymmetric multifractality and market efficiency of the major cryptocurrencies during the COVID-19 pandemic while accounting for different investment horizons. By applying the asymmetric multifractal detrended fluctuation analysis, we show that the outbreak affected the efficiency property of price behaviors differently between short- and long-term horizons. After the outbreak, the markets exhibited stronger multifractality in the short-term but weaker multifractality in the long-term. We also analyze asymmetric market patterns between upward and downward trends and between small and large price fluctuations and confirm that the outbreak has greatly changed the level of asymmetry in cryptocurrency markets.

Godfrey Uzonwanne

No abstract is available for this record.

Sergio Luis Náñez Alonso, Javier Jorge-Vázquez, Miguel Ángel Echarte Fernández, Ricardo Francisco Reier Forradellas

There are different studies that point out that the price of electricity is a fundamental factor that will influence the mining decision, due to the cost it represents. There is also an ongoing debate about the pollution generated by cryptocurrency mining, and whether or not the use of renewable energies will solve the problem of its sustainability. In our study, starting from the Environmental Performance Index (EPI), we have considered several determinants of cryptocurrency mining: energy price, how that energy is generated, temperature, legal constraints, human capital, and R&D&I. From this, via linear regression, we recalculated this EPI by including the above factors that affect cryptocurrency mining in a sustainable way. The study determines, once the EPI has been readjusted, that the most sustainable countries to perform cryptocurrency mining are Denmark and Germany. In fact, of the top ten countries eight of them are European (Denmark, Germany, Sweden, Switzerland, Finland, Austria, and the United Kingdom); and the remaining two are Asian (South Korea and Japan).

Zaghum Umar, Francisco Jareño, María de la O González

This research explores the impact of COVID-19-related media coverage on the dynamic return and volatility connectedness of the three dominant cryptocurrencies (Bitcoin (BTC), Ethereum (ETH) and Ripple (XRP)) and the fiat currencies of the euro, GBP and Chinese yuan. The sample period covers the first and second devasting waves of the COVID-19 pandemic crisis and ranges from January 1, 2020, to December 31, 2020. The dynamic return and volatility connectedness measures are estimated using the time varying parameter-VAR approach. Our return connectedness analysis shows that the media coverage index (only before the first wave) and the cryptocurrencies are the net transmitters of shocks while the fiat currencies are the net receivers of shocks. Similar results are obtained in terms of volatility, except for the euro, which shows a clear net receiver profile in January and February. This fiat currency (the euro) became a net transmitter in March and during the first wave of the COVID-19 crisis, which possibly shows the virulence of the pandemic on the European continent. Moreover, the most relevant differences between the net dynamic (return and volatility) connectedness of these two groups of currencies are focused on the beginning of the sample period, just before the first wave of the SARS-CoV-2 pandemic crisis, although some differences are observed during the first and second waves of the coronavirus outbreak.

José Álvarez‐Ramírez, Eduardo Pérez Rodríguez

No abstract is available for this record.