Blockchain Papers

Jia Ming Low, Zi Jian Tan, Tiong Yew Tang, Narishah Mohamed Salleh

No abstract is available for this record.

Chinh Nguyễn Lý Kiều, Anh Trần Thị Tuấn

Nghiên cứu này sử dụng các mô hình GARCH, bao gồm EGARCH(1,1), GJR-GARCH(1,1), TGARCH(1,1) và APARCH(1,1) để khảo sát sự bất đối xứng trong biến động tỷ suất sinh lợi của các loại tiền điện tử như Bitcoin, Ethereum, Ripple (XRP), Binance Coin (BNB) và DigiByte (DGB) trong khoảng thời gian từ ngày 01 tháng 01 năm 2018 đến ngày 31 tháng 5 năm 2023. Kết quả cho thấy mô hình EGARCH(1,1) là mô hình tốt nhất để mô tả hiệu ứng bất đối xứng trong biến động tỷ suất sinh lợi của các chuỗi tiền điện tử. Sự biến động tăng nhiều hơn trong phản ứng với cú sốc tích cực hơn là cú sốc tiêu cực, hàm ý một hiệu ứng bất đối xứng khác với hiệu ứng thường thấy trên thị trường chứng khoán. Kết quả nghiên cứu giúp nhà đầu tư và nhà quản lý rủi ro trong thị trường tiền điện tử hiểu rõ hơn về sự biến động giá, nhận biết, đánh giá rủi ro một cách chính xác hơn và đưa ra các chiến lược đầu tư phù hợp.

Fuzuli Aliyev, Ayşe Nur Şahinler, Orkhan Rustamov

This study examines the nexus between selected cryptocurrencies represented by Bitcoin and Ethereum and the expected market volatility denoted by the VIX index. We use daily data for the period of 06/01/2021 – 07/07/2023. Using Hong (2001) and Hong et al. (2009) Granger causality tests we find no causality in mean, however, we find strong bidirectional causality in variance for the variables at the 5% significance level across all lag lengths. At the time-varying causality analysis, we find BTC causes VIX in the 1st lag. The analysis findings have important implications for portfolio managers and potential investors. Although no direct causal relationship was found between Bitcoin and VIX, or Ethereum and VIX in terms of returns, the strong bidirectional causality in variance suggests that changes in volatility can affect related assets. This information can be utilized by portfolio managers to effectively assess mandates and manage portfolio risk.

Jiarui Fan, Zhenghao Wang

In recent years, cryptocurrencies have become more and more popular and recognized, and they have the characteristics of financial products and are involved in many financial transactions. Therefore, cryptocurrency trading is generally considered one of the most popular and promising types of profitability. However, this growing financial market is unstable, so this paper aims to develop a relatively accurate and reliable prediction model. This paper compares and analyzes various neural networks and traditional analytical methods based on the need to forecast cryptocurrency prices. Finally, this paper uses a variety of deep learning neural networks, including Recurrent neural network (RNN), Long Short-Term Memory (LSTM) network, Gate Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (BiLSTM), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). The data of three cryptocurrencies with the highest market capitalization, largest size, and best known were selected, namely Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB). We collect the three-cryptocurrency data for five consecutive years. The critical data collected includes daily (Eastern Standard Time) open, close, low, and high prices and the market capacity. The findings show that the CNN-LSTM neural network model has some limitations, such as the time lag in prediction. However, it can predict prices more accurately than other deep learning methods and have an overwhelming advantage over traditional time series methods.

Ludmila P. Bakumenko, Nadezhda S. Vasilyeva

In an age of rapidly changing technological revolutions, where cryptocurrencies and blockchain play key roles, studying the dynamics of cryptocurrency markets at the government level is becoming an urgent need, which is not just a step into the future, but also an opportunity for countries to act forward, based on data analysis and forecasting global economic trends. Every aspect of cryptocurrency - from financial stability to technological innovation - has the potential to transform the global landscape. Studying the interaction of cryptocurrencies with national interests will not only help to determine the positions of countries in this context, but also formulate effective strategies for managing this rapidly developing economic segment. It is important to realize that those states that integrate cryptocurrency market analysis into their strategies can best adapt to the challenges of the modern world and promote their economic prosperity. The purpose of the research is to study how the introduction of digital money into the economy affects the interest of various countries in participating in trading in the cryptocurrency market. To identify the relationship between the integration of such assets into the economy and the desire of host countries to participate in cryptocurrency markets. Consequently, there is a need to analyze the mechanisms of interaction of large economic entities - states - with cryptocurrencies, as well as predict the likely responses in this context of research. Using panel data analysis, to conduct a study of the dynamics of the cryptocurrency market in the digital finance market using the example of 50 countries around the world. To identify the relationship between the attitudes of countries and the dynamics of the cryptocurrency market in order to suggest possible directions for the future development of the studied evolutionary economic sphere. Materials and methods. As a basis for the study, a balanced and informative set of indexes (17 indexes) was identified, which represents the key variables necessary for a more in-depth analysis of the dynamics of cryptocurrency markets in the context of various countries over a period of ten years (2013-2022). The “Cryptocurrency trading volume” index was chosen as the effective index. The set of indexes was selected based on their ability to reflect cryptocurrency trading volumes, investor activity, and each country’s level of involvement in cryptocurrency transactions. The impact of various factors on the volume of transactions with electronic money and digital financial assets was assessed using panel data analysis methods in the Gretl statistical analysis program. Results. As a result of the analysis using the panel data tool, three models were created: a pooled regression model, a fixed-effects model, and a random-effects model. The choice of the best model is made through testing special hypotheses - the Brisch-Pagan test and the Hausman test. The fixed effects model was preferable to the random effects model in this study. The reason is the fixed effects model’s ability to take into account the individual characteristics of each country in the sample, leading to more accurate results. Based on the study of individual fixed effects, three groups of countries were identified: those that have a positive impact on the volume of cryptocurrency trading (for example, the United States and Japan), countries with a neutral impact (for example, Germany), and countries where individual effects have a negative impact (for example, China and Russia). Conclusion. Overall results indicate that countries with advanced digital infrastructure and ease of use of electronic payments, as well as inflationary and cultural influences, may exhibit higher activity in cryptocurrency markets. Based on the fixed effects model and taking into account assumptions about the dynamics in different countries, general conclusions were formulated regarding the index analyzed in this study - the volume of cryptocurrency trading.

Mohamed Shaker Ahmed, Elie Bouri

No abstract is available for this record.

Myriam Ben Osman, Emilios Galariotis, Khaled Guesmi, Haykel Hamdi · 5 authors

No abstract is available for this record.

Luca Galati, Francesco Capalbo

To what extent does the collapse of a commercial bank spread contagion across cryptocurrency markets? How do markets behave around bankruptcy if digital assets remain stuck within the bank and cannot be withdrawn? We use a BEKK model to examine contagion effects across major digital assets during the Silicon Valley Bank (SVB) collapse period in early March 2023. We find evidence of contagion across major stablecoins and Bitcoin. We also examine the price action when nearly all withdrawals at SVB were prohibited. We find substantial abnormal movements in stablecoin cumulative returns and volumes, indicating a “flight to safety” from less to more authoritative and trusted stablecoins. The implications for practitioners and policymakers are discussed.

Geoffrey Ngene, Jinghua Wang

No abstract is available for this record.

Zaghum Umar, Ahmed Bossman, Najaf Iqbal

No abstract is available for this record.

Ikhlaas Gurrib, Firuz Kamalov, Olga Starkova, Elgilani Elshareif · 5 authors

Purpose This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading? Design/methodology/approach Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted. Findings Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information. Originality/value To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information.

Małgorzata Just, Krzysztof Echaust

This study examines the role of cryptocurrencies as a hedging and safe-haven instrument against stock market risk. Employing five of the largest cryptocurrencies by market capitalization: BTC, ETH, BNB, ADA, and XRP, from 2017–2022 in a variance-optimal hedging framework we investigate and compare the hedging effectiveness of cryptocurrencies for the developed G7 and emerging BRICS stock markets. Based on EVT we introduced a new approach to the assessment of hedging effectiveness. We found that the probability of at least 10-percent hedging effectiveness of Bitcoin is approximately equal to zero. The conditional probability that Bitcoin can reduce at least 10% of volatility given that index returns fall below the 1st percentile is higher and ranges from 2% to 28.4% depending on the stock market. The probabilities estimated for other cryptocurrencies are lower. We provide new and valuable knowledge for investors, who consider cryptocurrencies as a shelter for their investment portfolios.

Rubaiyat Ahsan Bhuiyan, Tze Chi Chin, Ch. Zhang

<p class="MsoNormal" style="margin-top: 6.0pt;"><span lang="EN-US" style="mso-bidi-font-size: 10.5pt; font-family: 'Cambria',serif; mso-fareast-font-family: 宋体; mso-bidi-font-family: 'Times New Roman';">Gold has been traditionally well recognized as a safe heaven for financial markets. Lately, Bitcoin has been gradually considered as a popular alternative. Since the outbreak of COVID-19 in early 2020, it has become even more necessary and critical to examine the diversification capability of them to hedge financial risks associated with an unexpected crisis comparable to the pandemic. This paper hence employs the wavelet analysis, complemented by the multivariate DCC-GARCH approach, to measure the coherence of the gold and Bitcoin prices with six representative stock market indices, three for developed economies and three for emerging economies, all of which are heavily affected by the pandemic. To have a more balanced and comprehensive analysis, two-year data are used, spanning from 12th April 2019 to 15th April 2021, which covers approximately one year before and one year after the announcement of the COVID-19 pandemic. The results suggest that the returns of both gold and Bitcoin are generally not strongly correlated with the market returns of all six indices, particularly for short-term investment horizons. That is, investors in all six indices can benefit through gold, as well as Bitcoin, in terms of hedging. Meanwhile, compared with Bitcoin, gold shows to be less correlated with the indices, particularly for long-term investment horizons. The findings hence suggest that gold and Bitcoin offer diversification benefits to investors in the market indices during a crisis such as the COVID-19 pandemic, especially for short-term investment horizons. The study also reminds policymakers thinking beyond the pandemic about the future of the earth, including air pollution and health, for sustainable development of the whole world.</span></p>

Armin Mazinani, Luca Davoli, Gianluigi Ferrari

In recent years, cryptocurrencies have gained a lot of popularity in the financial markets and now, in addition to investing on them, it is possible to use them as a common currency to meet daily needs. Given the complex nature of financial markets and their reliance on different parameters to determine stocks' and assets' prices, the ability to predict prices is important for investment decisions, especially with respect to cryptocurrencies. To this end, Deep Learning (DL)-based algorithms can be viable solutions, owing to their use as time series forecasting tools. In this paper, we investigate the applicability of DL algorithms to forecast the prices of three cryptocurrencies, namely Bitcoin, Ethereum, and Ripple. We evaluate the performance of the proposed approach, in terms of short-term and long-term prediction accuracy (considering proper error metrics).

Xihan Xiong, Zhipeng Wang, William J. Knottenbelt, Michael Huth

Uniswap is currently the most liquid Decentralized Exchange (DEX) on Ethereum. In May 2021, it upgraded to the third protocol version named Uniswap V3. The key feature update is “concentrated liquidity”, which supports liquidity provision within custom price ranges. However, this design introduces a new type of Miner Extractable Value (MEV) source called Just-in-Time (JIT) liquidity attack, where the adversary mints and burns a liquidity position right before and after a sizable swap. We begin by formally defining the JIT liquidity attack and subsequently conduct empirical measurements on Ethereum. Over a span of 20 months, we identify 36,671 such attacks, which have collectively generated profits of 7,498 ETH. Our analysis suggests that the JIT liquidity attack essentially represents a whales' game, predominantly controlled by a select few bots. The most active bot, identified as 0xa57…6CF, has managed to amass 92% of the total profit. Furthermore, we find that this attack strategy poses significant entry barriers, as it necessitates adversaries to provide liquidity that is, on average, 269 times greater than the swap volume. In addition, our findings reveal that the JIT liquidity attack exhibits relatively poor profitability, with an average Return On Investment (ROI) of merely 0.007%. We also find this type of attack to be detrimental to existing Liquidity Providers (LPs) within the pool, as their shares of liquidity undergo an average dilution of 85%. On the contrary, this attack proves advantageous for liquidity takers, who secure execution prices that are, on average, 0.139% better than before. We further dissect the behaviors of the top MEV bots and evaluate their strategies through local simulation. Our observations reveal that the most active bot, 0xa57…6CF, conducted 27% of non-optimal attacks, thereby failing to capture at least 7,766 ETH (equivalent to 16.1M USD) of the potential attack profit.

Meltem Kılıç, Aydın Gürbüz, Nur Esra BEKERECİ

Bu çalışmada, Bitcoin fiyatları ile ekonomik politika belirsizlik endeksi (EPU), geniş para arzı (M3) ve enflasyon arasındaki ilişki ARDL sınır testi ve Toda-Yamamoto nedensellik testleri kullanarak araştırılmak istenmiştir. Bu bağlamda söz konusu değişkenler arasındaki kısa ve uzun dönem ilişkisi BRIC (Brezilya, Rusya, Hindistan ve Çin) ülkeleri açısından Ağustos 2010-Aralık 2021 arası aylık veriler kullanılarak gerçekleştirilmiştir. Ampirik analizler sonucunda Çin’nin EPU endeksinin uzun ve kısa dönemde Bitcoin’i negatif etkilediğine ulaşılmıştır. Hindistan için EPU endeksinin uzun dönemde Bitcoin fiyatı üzerindeki etkisi negatif iken; kısa dönemli etkiye rastlanılamadığı görülmüştür. Rusya ve Brezilya içinse EPU endeksi Bitcoin üzerinde etkili bulunamamıştır. BRIC ülkelerinde enflasyonun Bitcoin üzerindeki etkisi uzun dönemde pozitiftir. M3’ün Bitcoin üzerindeki etkisi Hindistan için kısa dönemde pozitif, Brezilya için uzun dönemde negatif yönlü çıkmıştır. Son olarak nedensellik sonuçlarına göre Hindistan ve Brezilya’da enflasyondan Bitcoin’e doğru tek yönlü nedensellik mevcuttur. Çin içinse enflasyondan Bitcoin’e; Bitcoin’den de ekonomik politika belirsizliğine doğru nedensellik ilişkisi söz konusudur. Elde edilen bulgular Bitcoin yatırımcılarının ve politika yapıcıların M3, enflasyon ve EPU’nun etkilerini göz önünde bulundurarak girişimde bulunmalarına ve Bitcoin’le ilgili düzenlemeler geliştirmelerine katkıda bulunacaktır.

Emre Ünal, Nezir Köse

The digital world has become an inevitable part of daily life. With cryptocurrencies, a new investment opportunity has emerged around the globe. Extending digital life increases the energy demand. These new assets consume a considerable amount of energy resources. The mining process in particular can be significantly affected by energy prices. The purpose of this work is to reveal the impact of the oil price on mineable and non-mineable cryptocurrencies which would provide insight for policymakers, investors, miners, and portfolio managers. This research utilized a panel cointegration model and panel Granger causality tests to the daily data collected between May 11, 2021 and June 23, 2022. 15 mineable and 19 non-mineable cryptocurrencies were selected for the study. Other variables include the oil price, the VIX, and the gold price. The research indicated that there is a negative correlation between the oil price and cryptocurrencies. The VIX had a negative effect in the short term, whereas the gold price had a positive and significant correlation in the long term. The impact of the oil price on mineable cryptocurrencies was larger than that on non-mineable cryptocurrencies. This means that alternative energy resources are essential to reduce the dependency of cryptocurrencies on this type of fossil fuel.

Samet Günay, Buket Kırcı Altınkeski, Emrah İsmail Çevik, John W. Goodell

No abstract is available for this record.

Wissal Zribi, Talel Boufateh, Khaled Guesmi

No abstract is available for this record.

Meghavi Rana, Sumedha Bhardwaj, Ishaan Dawar, Vipul Gupta · 6 authors

The emergence of cryptocurrencies has significantly impacted the global economy and disrupted the established banking structures. However, the decentralised structure of cryptocurrencies and the lack of governmental control have brought about several hazards that require careful analysis and efficient management. This paper presents a comprehensive risk analysis of cryptocurrencies with a focus on the difficulties encountered and potential future options for risk mitigation. This study examines the hazards associated with cryptocurrency, considering elements such as techniques, tools, advantages, and disadvantages of available publications. It assesses how these risks affect a variety of stakeholders, such as investors and companies. This paper also explores the concept and adopts currently used by financial institutions and industry individuals in risk management. It assesses how well these techniques work to reduce the identified risks while highlighting their inherent drawbacks. It emphasizes the value of encouraging interdisciplinary collaboration among academia, industry specialists, and regulatory organizations to address the changing issues and create a future landscape of Bitcoin risk management.

Pham Thi Ngoc Dung, Long Luong, Le Ngoc Thuy Trang, Do Thi Thanh Nhan

This study aims to analyze the role of bitcoin and gold as safe haven assets against Asian equity markets during periods of high market uncertainty related to the global COVID-19 pandemic, high volatility, and extreme stock market conditions. Empirical analysis employ the DCC-GARCH methodology to estimate the time-varying relationship between bitcoin/ gold and the Asian stock market from 2016 to 2023. Our findings reveal that bitcoin serves as a strong hedge for Taiwan and Pakistan, whereas gold can be considered as a strong hedge for Japan, Singapore, India, Thailand and Vietnam. Interestingly, we observed that bitcoin does not exhibit safe haven properties in any of the Asian countries observed. In contrast, gold demonstrates strong safe haven abilities for Singapore, India, and Thailand. These results remain consistent across various measures of market turmoil, including the volatility index, COVID-19-related periods, and low quantiles in the stock market. Furthermore, our results suggest that the perception and adoption of gold as a safe haven asset in Japan and Vietnam is mainly influenced by global events and uncertainties, rather than localized stock market conditions. These findings offer valuable information for investors, financial institutions, as well as policy makers and regulators, on how cryptocurrency and gold evolved as hedge and safe haven assets in Asia during uncertainty periods.

Phasin Wanidwaranan, Santi Termprasertsakul

Purpose This study examines herd behavior in the cryptocurrency market at the aggregate level and the determinants of herd behavior, such as asymmetric market returns, the coronavirus disease 2019 (COVID-19) pandemic, 2021 cryptocurrency's bear market and the network effect. Design/methodology/approach The authors applied the Google Search Volume Index (GSVI) as a proxy for the network effect. Since investors who are interested in a particular issue have a common interest, they tend to perform searches using the same keywords in Google and are on the same network. The authors also investigated the daily returns of cryptocurrencies, which are in the top 100 market capitalizations from 2017 to 2022. The authors also examine the association between return dispersion and portfolio return based on aggregate market herding model and employ interactions between herding determinants such as, market direction, market trend, COVID-19 and network effect. Findings The empirical results indicate that herding behavior in the cryptocurrency market is significantly captured when the market returns of cryptocurrency tend to decline and when the network effect of investors tends to expand (e.g. such as during the COVID-19 pandemic or 2021 Bitcoin crash). However, the results confirm anti-herd behavior in cryptocurrency during the COVID-19 pandemic or 2021 Bitcoin crash, regardless of the network effect. Practical implications These findings help investors in the cryptocurrency market make more rational decisions based on their determinants since cryptocurrency is an alternative investment for investors' asset allocation. As imitating trades lead to return comovement, herd behavior in the cryptocurrency has a direct impact on the effectiveness of portfolio diversification. Hence, market participants or investors should consider herd behavior and its underlying factors to fully maximize the benefits of asset allocation, especially during the period of market uncertainty. Originality/value Most previous studies have focused on herd behavior in the stock market. Although some researchers have recently begun studying herd behavior in the cryptocurrency market, the empirical results are inconclusive due to an incorrectly specified model or unclear determinants.

John Kingsley Woode, Peterson Owusu, Anokye M. Adam, Emmanuel Assifuah-Nunoo · 5 authors

The study extends the literature on the nexus between cryptocurrency and uncertainty. This study proxied the cryptocurrencies and global uncertainty, respectively, with the seven most significant and variationally susceptible cryptos and the comprehensive world uncertainty in measuring the crypto-uncertainty nexus over the period (2015–2022) and further employing the quantile regression approach. The OLS model results point to a blend of both significant and insignificant relationship between global uncertainty and cryptocurrencies. These relationships were further examined in quantiles and further accounted for the impact of investor sentiments (VIX) and volatility (OVX), and the results were largely corroborated with the results from the conventional OLS, except for the Bitcoin, Litecoin, and Ripple markets. It was also discovered that the nexus changes across quantiles. The results revealed a blend of strong and weak hedges and safe havens among the selected cryptos against global uncertainty during normal and extreme market conditions. In the face of global turmoil, it was revealed that the average crypto market could serve as a safe haven. Also, the cryptos with an insignificant nexus with global uncertainty were found to be significantly affected by investor sentiment. These findings were further confirmed by the quantile-on-quantile and causality-in-quantile estimations. Given the intense precariousness and lack of hedge and haven capacities within the majority of the cryptocurrencies, it is pertinent for investors to consider the market in general as a means of diversifying their portfolios and reserve the hedge and haven option to the few markets that possess such luxury.

Walter Bazán-Palomino

No abstract is available for this record.