Blockchain Papers

Ahmed Bouteska, M. Kabir Hassan, Mamunur Rashid, Mehmet Hüseyin Bilgin

No abstract is available for this record.

Donia Aloui, Riadh Zouaoui, Houssem Rachdi, Khaled Guesmi · 5 authors

In this paper, we investigate non-linear linkages between Bitcoin and the unconventional monetary policies of the European Central Bank (ECB). In particular, we examine whether a low-interest rate environment resulting from QE indirectly encourages investors to move towards Bitcoin. Using a Bayesian VAR model with time-varying coefficients and stochastic volatility (TVP-BVAR-SV model), we compare Bitcoin’s responses to the shadow rate shocks during the pre-and post-COVID-19 periods. Moreover, despite the high uncertainty and the low-interest rate environment, Bitcoin's response during the COVID-19 period reveals a steeper drop compared to the pre-COVID-19 period. That said, investors did not resort to Bitcoin for safety and higher returns. Our findings can be attributed to the unprecedented nature of the crisis, the investor reluctance and pessimism, and the changing behavior of Bitcoin, which is no longer perceived as a safe haven.

Afzol Husain

This research studies the dynamic connectedness among digital assets proxied by non-fungible tokens (NFTs), Islamic cryptocurrencies, and conventional cryptocurrencies with the US Economic Policy Uncertainty (EPU) and Geopolitical Risk (GPR) indices. We also examine the hedge and safe haven properties of the aforementioned digital assets against the uncertainties. Using wavelet coherence analysis from 19 January 2018 to 31 October 2023, we show that NFTs react heterogeneously to changes in uncertainties while cryptocurrency reacts inversely. NFTs and conventional cryptocurrencies can only act as diversifiers, but neither as a hedge nor a safe haven against uncertainties. However, Islamic cryptocurrencies have the potential to act as both a hedge and a safe haven against uncertainties. Our findings shed light on the role of emerging digital assets in formulating investment strategies and ensuring stability in the financial markets. Originality/Value: Given the immense potential of digital assets, a remaining research gap concerns their interplay with uncertainty. In other words, given the presence of extreme market turmoil over recent years, no consensus is present in terms of highlighting the dynamic co-movement between digital assets such as NFT, Islamic cryptocurrencies, and global uncertainty factors. In addition to that, the lead-lag relationship among digital assets and uncertainties are also unknown till date. The current study fills this gap by providing robust evidence.

Ahmet Faruk Aysan, Erhan Muğaloğlu, Ali Yavuz Polat, Hasan Tekin

Abstract Using a wavelet coherence approach, this study investigates the relationship between Bitcoin return and Bitcoin-specific sentiment from January 1, 2016 to June 30, 2021, covering the COVID-19 pandemic period. The results reveal that before the pandemic, sentiment positively drove prices, especially for relatively higher frequencies (2–18 weeks). During the pandemic, the relationship was still positive, but interestingly, the lead-lag relationship disappeared. Employing partial wavelet tools, we factor out the number of COVID-19 cases and deaths and the Equity Market Volatility Infectious Disease Tracker index to observe the direct relationship between a change in sentiment and return. Our results robustly reveal that, before the pandemic, sentiment had a positive effect on return. Although positive coherence still existed during the pandemic, the lead-lag relationship disappeared again. Thus, the causal relationship that states that sentiment leads to return can only be integrated into short-term trading strategies (up to six weeks frequency).

Shoaib Ali, Muhammad Naveed, Imran Yousaf, Muhammad Sualeh Khattak

No abstract is available for this record.

Umawadee Detthamrong, Seksak Prabpala, Akkharawoot Takhom, Nattapong Kaewboonma · 6 authors

This study examines the causal relationship between cryptocurrencies and other major world economic assets, such as gold, stocks, oil, and bonds, using both Granger causality and correlation analyses. The study focuses on the period between 2018 and 2022, using a vector autoregressive model (VAR) to analyze data on cryptocurrencies and other major world economic assets, which collectively represent over 90% of the market during the observed period. Results show that correlation clearly identifies causal interdependency between cryptocurrencies and other major world economic assets and that the variation in cryptocurrencies increasingly explains other major world economic assets. The results reveal that there is Granger causality between the cryptocurrencies (Tether, USD Coin, and Binance USD) and the other major world economic assets (BOND, SP500, and GOLD). Additionally, the study finds evidence that market inefficiency in the cryptocurrency market increased between 2018 and 2022. The findings suggest that the properties of the cryptocurrency market are highly dynamic and that researchers should be hesitant to generalize the market properties observed during idiosyncratic periods. The relevant information is swiftly reflected in asset prices when investors are more interested in a news event, increasing volatility. Strong evidence suggests that volatility spill overs increase sharply at this time. The structure of these markets frequently changes, and a large number of cryptocurrencies appear and disappear every day.

Haolin Tian

The Bitcoin price was chosen as the research subject, and the observation period was set from January 2015 to September 2023. An ARIMA time series model was constructed to forecast the trading price. The results indicate that the optimal model for fitting the trading price is ARIMA (3, 2, 8). This model takes into account trends, seasonality, and other factors that may impact the price of Bitcoin. By analyzing the historical data, the model was able to accurately predict the short-term fluctuations in Bitcoin’s trading price. Based on this, short-term predictions were made for Bitcoin’s trading price in the next year. Recommendations were then provided by combining the forecast results with the economic development situation in the post-pandemic era. The recommendations suggest that Bitcoin has become a low-quality asset and is no longer suitable for diversifying one’s investment portfolio, but rather focus on the development of physical industries and adjust one’s investment portfolio in a timely manner.

Suleiman Dahir Mohamed, Mohd Tahir Ismail, Majid Khan Majahar Ali

This study finds breaks, trend breaks, and outliers in the last decade returns of five cryptocurrencies Bitcoin, Ethereum, Litecoin, Tether USD, and Ripple that experienced frequent changes. The study uses the indicator saturation (IS) approach to simultaneously identify breaks, trend breaks, and outliers in these returns to gain a deeper understanding in their dynamics. The study found that monthly, weekly and daily breaks existed in these returns as well as trend breaks, and outliers mostly during the market peaks in 2017, 2018, 2020, and 2021 that can be attributed to a number of things, such as the global Covid-19 pandemic in 2020, the 2021 crypto crackdown in China, the 2020 price halving of Bitcoin, and the 2017–2018 initial coin offering (ICO) boom. These returns also have common break segments and outliers. The application of IS technique to cryptocurrencies and simultaneous detection of market breaks, trend breaks, and outliers makes this study unique. This study is limited to considering only returns of five digital coins. These results may help traders, investors, and financial analysts modify their tactics and risk-management techniques to deal with the complexity of the cryptocurrency market.

Xiaoyun Lin, Yuhan Xia, Yuxuan Lu, Zeshuo Chen

Bitcoin is a digital currency created by a large number of calculations based on a specific algorithm. With the time development, more investors came into the market and the price of the bitcoin had been changing all the time. But bitcoin investors want to be able to predict price fluctuations because they don't want to lose their profits. This paper uses machine learning and artificial intelligence to make some reasonable predictions about Bitcoin price fluctuations.

Fatma Ben Hamadou, Taicir Mezghani, Mouna Boujelbène Abbes

Understanding the interplay between investor sentiment and cryptocurrency returns has become a critical area of research. Indeed, this study aims to uncover the role of Google investor sentiment on cryptocurrency returns (including Bitcoin, Litecoin, Ethereum, and Tether), especially during the 2017-18 bubble (January 01, 2017, to December 31, 2018) and the COVID-19 pandemic (January 01, 2020, to March 15, 2022). To achieve this, we use two techniques: quantile causality and wavelet coherence. First, the quantile causality test unveils that investors’ optimistic sentiments have notably higher cryptocurrency returns, whereas pessimistic sentiment has significantly opposite effects. Moreover, the wavelet coherence analysis shows that co-movement between investor sentiment and Tether cannot be considered significant. This result supports the role of Tether as a stablecoin in portfolio diversification strategies. In fact, the findings will help investors improve the accuracy of cryptocurrency return forecasts in times of stressful events and pave the way for enhanced decision-making utility.

D.M.D.K. Dasanayake, H.Y. Dilshan, H.D.K.Y. Rathnaweera, Sapumal Ahangama · 5 authors

The cryptocurrency and stock markets are dynamic environments that attract traders, seeking to enhance their investment returns. In cryptocurrency trading, there is a pullback in investors from trading due to recent market crashes, losses, and bankruptcies. For anticipating future market behavior, algorithmic trading has gained popularity due to its ability to provide consistent and accurate price and volatility predictions. Specifically, the bottom turning points of the market are where an investor can use to enter the market. Hence, identifying market turning points, particularly market bottoms, is vital in timing trading strategies for a maximum profit. This study introduces a novel and ground-breaking approach to market forecasting that focuses on identifying market bottoms, particularly in the domain of cryptocurrency trading. The study utilizes a Wasserstein Generative Adversarial Network (WGAN) with Gated Recurrent Unit (GRU) to identify future market trends effectively. A classifier is added into the model as a substantial contribution to forecast future market bottoms by utilizing hidden WGAN features. The research findings indicate that the combination of the price prediction and bottom classification models provides outperforming results in terms of prediction accuracy. In addition, the suitability of the proposed solution for locating stock market bottoms has been evaluated.

Shravya Barla

Abstract: The goal of this project is to use machine learning to forecast cryptocurrency values. As a result of their high levels of volatility, cryptocurrencies are notoriously difficult to anticipate in terms of value. SARIMA (Seasonal Auto Regressive Integrated Moving Average) algorithm that we suggest using to capture the intricate dynamics of the bitcoin market. Our machine learning models will be trained using the gathered data, and they will then be utilised to forecast future cryptocurrency values. The project's final product is anticipated to be a useful tool for cryptocurrency traders, analysts, and investors, giving them a more precise way to make data-drive investment decisions.

Dirk G. Baur, Lai T. Hoang

No abstract is available for this record.

Mst Shapna Akter, Hossain Shahriar, Md. Abdur Rahman, Muhammad Sabbir Rahman · 5 authors

In recent times, the cryptocurrency market has emerged as one of the fastest-growing financial markets worldwide. It is, however, known for its high volatility and illiquidity compared to traditional markets such as equities, foreign exchange, and commodities. This inherent risk creates uncertainty among investors. The aim of this research is to forecast the level of risk in the cryptocurrency market. To assist cryptocurrency investors in navigating these challenges, we propose an approach that involves calculating the risk factor based on existing parameters. We employed various machine learning algorithms, including CNN, LSTM, BiLSTM, and GRU, to predict the risk factor in twenty elements of the cryptocurrency market. Through extensive experimentation, we developed a new model that outperformed existing models, achieving the highest Root Mean Square Error (RMSE) value of 1.3229 and the lowest RMSE value of 0.0089. Furthermore, we tested the generalization ability of our proposed model on a new dataset, different from the one used for training. Even with this new dataset, our model displayed robust performance. In contrast, the other existing models achieved higher RMSE values, with the highest being 14.5092 and the lowest 0.02769. By adopting our approach, investors can trade more confidently in complex and challenging financial assets such as Bitcoin, Ethereum, and Dogecoin. Our proposed model demonstrates superior performance and generalization capabilities, providing valuable insights for participants in the cryptocurrency market.

Andrew Urquhart, Larisa Yarovaya

Since Bitcoin was first proposed in late 2008 and went live in 2009, hundreds of research papers have been published trying to understand the behaviour of cryptocurrencies and their impact on financial markets. Their size and importance to the financial sector has increased substantially also has the number of challenges they face and the negative externalities they have caused. This article reviews the related cryptocurrency literature and introduces articles included in this special issue on this theme which were presented at the 2020 Cryptocurrency Research Conference. We conclude by offering possible future research directions.

Don Charles

This study seeks to investigate how distributed ledger technology can be applied to the green bond market. Second, this study examines how green bonds can finance the suck cost of decarbonizing the ammonia industry. Third, this study seeks to forecast the spot price of ammonia. This forecast is relevant since the bond’s coupon should be indexed and linked to the price of ammonia. The proposed tokenized indexed-green bond is a new idea that leverages the technologies of distributed ledgers, indexation, and green bonds. No study to current date has undertaken such research that integrates these technologies to fund the decarbonization of the ammonia industry. Data was collected on the spot price of ammonia from the Central Bank of Trinidad and Tobago online database at the monthly frequency over the January 1991 to June 2023 period. The applied forecasting methodology was a hybrid framework combining Particle Swarm Optimization and Support Vector Regression. This study found that an out-of-sample forecast for ammonia prices would be US$438.89/ton in the 1st quarter, US$289.99/ton by the 2nd quarter, US$448.30/ton by the 3rd quarter, and US$331.57/ton by the 4th quarter. The decarbonization of the ammonia industry is technically possible. Economically, it would involve leveraging several technologies such as green bond financing, tokenization, and indexation. Received: 26 May 2023 | Revised: 1 September 2023 | Accepted: 3 December 2023 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Don Charles: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, resources, data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration.

André D. Gimenes, Jéfferson Augusto Colombo, Imran Yousaf

Abstract In this study, we analyze the stock market reaction to 35 events associated with 32 publicly traded companies from six countries that have announced cryptocurrency acquisitions, selling, or acceptance as a means of payment. Our analysis focuses on traditional firms whose core business is unrelated to blockchain or cryptocurrency. We find that the aggregate market reaction around these events is slightly positive but statistically insignificant for most event windows. However, when we perform heterogeneity analyses, we observe significant differences in market reaction between events with high (larger CARs) and low cryptocurrency exposure (lower CARs). Multivariate regressions show that the level of exposure to cryptocurrency ("skin in the game") is a critical factor underlying abnormal returns around the event. Further analyses reveal that economically meaningful acquisitions of BTC or ETH (relative to firm's total assets) drive the observed effect. Our findings have important implications for managers, investors, and analysts as they shed light on the relationship between cryptocurrency adoption and firm value.

Julien Chevallier, Bilel Sanhaji

In this paper, we conducted an empirical investigation of the realized volatility of cryptocurrencies using an econometric approach. This work’s two main characteristics are: (i) the realized volatility to be forecast filters jumps, and (ii) the benefit of using various historical/implied volatility indices from brokers as exogenous variables was explicitly considered. We feature a jump-robust extension of the REGARCH-MIDAS-X model incorporating realized beta GARCH processes and MIDAS filters with monthly, daily, and hourly components. First, we estimated six jump-robust estimators of realized volatility for Bitcoin and Ethereum that were retained as the dependent variable. Second, we inserted ten Bitcoin and Ethereum volatility indices gathered from various exchanges as an exogenous variable, each at a time. Third, we explored their forecasting ability based on the MSE and QLIKE statistics. Our sample spanned the period from May 2018 to January 2023. The main result featured the best predictors among the volatility indices for Bitcoin and Ethereum derived from 30-day implied volatility. The significance of the findings could mostly be attributable to the ability of our new model to incorporate financial and technological variables directly into the specification of the Bitcoin and Ethereum volatility dynamics.

Ali Fereydooni, Ehsan Hajizadeh

Finding suitable safe haven opportunities to protect emerging investments, such as cryptocurrencies, from external factors, such as Geopolitical risk, is a major concern for investors. Recognizing safe havens for these assets can help investors and traders manage risk, stabilize their portfolios, diversify their investments, and preserve capital against Geopolitical risk. To find the safe havens for cryptocurrencies regarding Geopolitical risk, this study proposes an approach to identifying the most suitable safe havens for cryptocurrencies highly affected by Geopolitical risk. First, the study identifies the cryptocurrencies that are more influenced by Geopolitical risk than others; by this, the assets that require hedging are discovered. Then, a new method, quantile-on-quantile regression, is employed to test the hedging ability of multiple assets from different markets. Once the outcomes of the quantile-on-quantile regression are cleared, the hedge effectiveness index by dynamic conditional correlation GARCH is calculated to validate the results. Both methods yield similar results, suggesting that the Forex market and stock indexes are the most suitable options as safe havens for cryptocurrencies. The study also finds that assets from the energy sector of the commodity market, such as Crude Oil and Natural Gas, are the weakest safe haven options.

Onur Özdemir, Anoop Kumar

No abstract is available for this record.

Kuo‐Shing Chen, Wei-Chen Ong

<abstract> <p>In this paper, we aim to uncover the dynamic spillover effects of Bitcoin environmental attention (EBEA) on major asset classes: Carbon emission, crude oil and gold futures, and analyze whether the integration of Bitcoin into portfolio allocation performance. In this study, we document the properties of futures assets and empirically investigate their dynamic correlation between Bitcoin, carbon emission, oil and gold futures. Overall, it is evident that the volatility of Bitcoin, as well as other prominent returns, exhibit an asymmetric response to good and bad news. Additionally, we evaluate the hedge potential benefits of these emerging futures assets for market participants. The evidence supports the idea that the leading cryptocurrency-Bitcoin can be a suitable hedge instrument after the COVID-19 pandemic outbreak. More importantly, our analysis of the portfolio's performance shows that carbon emission futures are diversification benefit products in most of the considered cases. Notably, incorporating carbon futures into portfolios may attract new investors to carbon markets for double goals of risk diversification. These findings also provide insightful evidence to investors, crypto traders, and portfolio managers in terms of hedging strategy, diversification and risk aversion <sup>[<xref ref-type="bibr" rid="b19">19</xref>,<xref ref-type="bibr" rid="b20">20</xref>,<xref ref-type="bibr" rid="b21">21</xref>,<xref ref-type="bibr" rid="b22">22</xref>,<xref ref-type="bibr" rid="b23">23</xref>,<xref ref-type="bibr" rid="b24">24</xref>,<xref ref-type="bibr" rid="b25">25</xref>]</sup>.</p> </abstract>

Belén Gill de Albornoz Noguer, Juan Ángel Lafuente, Mercedes Monfort, Javier Ordóñez

This paper explores the role of Economic Policy Uncertainty (EPU) as driver of the Bitcoin public attention. Using Google trends data from January 2010 to November 2021 in a set of 22 countries, a Principal Components Analysis reveals a strong unique commonality on the internet searching patterns for Bitcoin across countries, which suggests that the potential explaining factors of the Bitcoin attention should be global instead of local. The multivariate analysis corroborates this hypothesis since EPU at the country level does not play a significant role in explaining the searching patterns on Google for Bitcoin, while the global EPU does.

Metin KILIÇ, İnci Merve ALTAN

Cryptocurrencies, which started with Bitcoin, which was released differently from traditional payment and investment tools, have large transaction volumes today. In addition to the many economic benefits of cryptocurrencies, which are used both as a payment tool and as a financial investment tool, high energy consumption and a heavy carbon footprint come with them. With the owner of the automaker Tesla stating that he is worried about the increasing use of fossil fuels in Bitcoin mining and cutting its support for Bitcoin, the price of Bitcoin has fallen sharply, while green cryptocurrencies have reached historical peaks. This situation reminded the investors that they should handle risky investments carefully and also highlighted the importance of green investment tools. Understanding the relationship between green cryptocurrencies and other assets is essential for investors looking to expand their portfolios and seize emerging opportunities. In this direction, the study examined whether green cryptocurrencies are a safe haven against non-green cryptocurrencies in the period of January 2022–July 2023. In the analysis, DCC-GARCH analysis, risk, and return analyses were performed for safe haven. According to the analysis' findings, among cryptocurrencies, green cryptocurrencies are most likely to be a safe haven for investors.

Chunshuang Ye, Yan‐Kai Fu, Tiantian Wang, Qing Lu

No abstract is available for this record.