The blossoming of cryptocurrencies during the last decade has largely influenced both the financial and the technological world. Bitcoin emerged on the edge of the financial crisis in 2008, signaling the very beginning of a financial and technological innovation, which in continuance would eventually create a lot of questions and debate previously unforeseeable. This paper aims to explore the impact of factors such as trading volume, information demand, stock returns, and exchange rates on the volatility of returns for decentralized and unbacked cryptocurrencies from 2016 to 2022 by employing the GARCH model. Based on each coin’s innate functional characteristics and market performance quantified by their respective market capitalization, the selection included Bitcoin, Ether, and XRP as representative crypto coins for the category of decentralized and unbacked cryptocurrencies. The implementation of correlation analysis and the use of the GARCH model on influencing factors for each coin revealed that decentralized and unbacked cryptocurrencies are positively related to trading volume, information demand, and exchange rates while being indifferent to a certain extent to the stock market returns of the world stock index MSCI ACWI. The results of this study provide further insight into the behavior of cryptocurrency return volatility in the new, ever-changing, and highly unpredictable crypto market as well as aid investors in their decision-making process concerning portfolio optimization.
Using the Bollinger Bands trading strategy (BBTS), investors are advised to buy (and then sell) Bitcoin and Ethereum spot prices in response to BBTS’s oversold (overbought) signals. As a result of analyzing whether investors would profit from round-turn trading of these two spot prices, this study may reveal the following remarkable outcomes and investment strategies. This study first demonstrated that using our novel design with a heatmap matrix would result in multiple higher returns, all of which were greater than the highest return using the conventional design. We contend that such an impressive finding could be the result of big data analytics and the adaptability of BBTS in our new design. Second, because cryptocurrency spot prices are relatively volatile, such indices may experience a significant rebound from oversold to overbought BBTS signals, resulting in the potential for much higher returns. Third, if history repeats itself, our findings might enhance the profitability of trading these two spots. As such, this study extracts the diverse trading performance of multiple BB trading rules, uses big data analytics to observe and evaluate many outcomes via heatmap visualization, and applies such knowledge to investment practice, which may contribute to the literature. Consequently, this study may cast light on the significance of decision-making through the utilization of big data analytics and heatmap visualization.
The purpose of this study is to examine the market efficiency of cryptocurrencies, specifically at a weak level. The study focuses on six prominent cryptocurrencies selected based on their significant market capitalization: Bitcoin (BTC), Tether (USDT), Ethereum (ETH), Binance Coin (BNB-USD), Ripple (XRP-USD), and Cardano USD (ADA-USD). The analysis utilizes unit root, Ljung–Box, variance ratio, runs, and the Brock–Dechert–Scheinkman (BDS) tests to assess different aspects of market efficiency. The data spans from September 2017 to April 2023, encompassing a wide time frame to capture potential shifts in market behavior. The results of all the tests, except the BDS test, indicate that the tested cryptocurrencies' markets are inefficient. However, the BDS test yielded different results, suggesting that BTC and ETH exhibit market efficiency compared to the other cryptocurrencies. This discrepancy indicates that the BDS test may be capturing different aspects of the time series behavior. The practical implication is that investors and market participants should exercise caution and consider the varying levels of efficiency when making decisions regarding these cryptocurrencies. Also, investors should consider a range of factors, including technical and fundamental analyses, when making investment decisions in a dynamic and evolving market.
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.
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.
Bu çalışmada altın ile kripto paralar arasındaki ilişkiler doğrusal olmayan modeller ile kapsamlı olarak araştırılmaktadır. Kripto paraları temsilen dijital altın olarak da adlandırılan en büyük kripto para Bitcoin ve en büyük akıllı kontrat platformu Ethereum çalışmada birlikte ele alınmaktadır. Hepsağ (2021) doğrusal olmayan eşbütünleşme testi bulgularına göre, ilgili değişkenler arasında çok zayıf düzeyde uzun dönemli ilişki, doğrusal olmayan Granger nedensellik testi sonuçlarına göre ise iki yönlü nedensellik ilişkisi tespit edilmiştir. Son olarak düzeltilmiş dinamik koşullu korelasyon (cDCC-GARCH) sonuçlarına göre altın ve kripto paralar arasında genellikle pozitif ve sıfıra yakın korelasyon bulunduğu, ancak COVID-19 salgınının görüldüğü 2020 yılı boyunca değişkenler arasındaki korelasyon ilişkisinin daha da arttığı belirlenmiştir. Elde edilen bulgular yatırımcılar için portföy çeşitlendirmesi, risk yönetimi ve piyasa öngörüsü açısından önemli bilgiler sunmaktadır.
Cryptocurrencies have increasingly attracted the attention of several players interested in crypto assets. Their rapid growth and dynamic nature require robust methods for modeling their volatility. The Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model is a well-known mathematical tool for predicting volatility. Nonetheless, the Realized-GARCH model has been particularly under-explored in the literature involving cryptocurrency volatility. This study emphasizes an investigation on the performance of the Realized-GARCH against a range of GARCH-based models to predict the volatility of five prominent cryptocurrency assets. Our analyses have been performed in both in-sample and out-of-sample cases. The results indicate that while distinct GARCH models can produce satisfactory in-sample fits, the Realized-GARCH model outperforms its counterparts in out of-sample forecasting. This paper contributes to the existing literature, since it better reveals the predictability performance of Realized-GARCH model when compared to other GARCH-types analyzed when an out-of-sample case is considered.
Sulalitha Bowala, A. Thavaneswaran, Ruppa K. Thulasiram, Md Erfanul Hoque · 5 authors
Trading volume is an important variable to successfully capture market risks along with asset price/returns. Recently, there has been a growing interest in deep learning methods to forecast the trading volume of stocks using historical volatility as a feature. Unlike the existing work, a novel datadriven log volatility forecast is proposed in this paper as an extra feature to improve trading volume forecasts. Recently, neural networks for volatility and neural nets for electricity demand forecasting, constructed with nnetar function, have shown to be superior. The novelty of this paper is to demonstrate the neural network based on the nnetar function from the forecast package in R for trading volume forecast shows superiority over the other neural network.
Pavlos I. Zitis, Shinji Kakinaka, Ken Umeno, Stavros G. Stavrinides · 6 authors
The COVID-19 pandemic has had an unprecedented impact on the global economy and financial markets. In this article, we explore the impact of the pandemic on the weak-form efficiency of the cryptocurrency and forex markets by conducting a comprehensive comparative analysis of the two markets. To estimate the weak-form of market efficiency, we utilize the asymmetric market deficiency measure (MDM) derived using the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) approach, along with fuzzy entropy, Tsallis entropy, and Fisher information. Initially, we analyze the temporal evolution of these four measures using overlapping sliding windows. Subsequently, we assess both the mean value and variance of the distribution for each measure and currency in two distinct time periods: before and during the pandemic. Our findings reveal distinct shifts in efficiency before and during the COVID-19 pandemic. Specifically, there was a clear increase in the weak-form inefficiency of traditional currencies during the pandemic. Among cryptocurrencies, BTC stands out for its behavior, which resembles that of traditional currencies. Moreover, our results underscore the significant impact of COVID-19 on weak-form market efficiency during both upward and downward market movements. These findings could be useful for investors, portfolio managers, and policy makers.
Nir Chemaya, Lin William Cong, Emma Jorgensen, Dingyue Liu · 5 authors
Decentralized Finance (DeFi) is reshaping traditional finance by enabling direct transactions without intermediaries, creating a rich source of open financial data. Layer 2 (L2) solutions are emerging to enhance the scalability and efficiency of the DeFi ecosystem, surpassing Layer 1 (L1) systems. However, the impact of L2 solutions is still underexplored, mainly due to the lack of comprehensive transaction data indices for economic analysis. This study bridges that gap by analyzing over 50 million transactions from Uniswap, a major decentralized exchange, across both L1 and L2 networks. We created a set of daily indices from blockchain data on Ethereum, Optimism, Arbitrum, and Polygon, offering insights into DeFi adoption, scalability, decentralization, and wealth distribution. Additionally, we developed an open-source Python framework for calculating decentralization indices, making this dataset highly useful for advanced machine learning research. Our work provides valuable resources for data scientists and contributes to the growth of the intelligent Web3 ecosystem.
Purpose This study provides a comprehensive analysis of the potential contagion of Bitcoin on financial markets and sheds light on the complex interplay between technological advancements, accounting regulatory and financial market stability. Design/methodology/approach The study employs a multi-faceted approach to analyze the impact of BTC systemic risk, technological factors and regulatory variables on Asia–Pacific financial markets. Initially, a single-index model is used to estimate the systematic risk of BTC to financial markets. The study then uses ordinary least squares (OLS) to assess the potential impact of systemic risk, technological factors and regulatory variables on financial markets. To further control for time-varying factors common to all countries, a fixed effect (FE) panel data analysis is implemented. Additionally, a multinomial logistic regression model is utilized to evaluate the presence of contagion. Findings Results indicate that Bitcoin's systemic risk to the Asia–Pacific financial markets is relatively weak. Furthermore, technological advancements and international accounting standard adoption appear to indirectly stabilize these markets. The degree of contagion is also found to be stronger in foreign currencies (FX) than in stock index (INDEX) markets. Research limitations/implications This study has several limitations that should be considered when interpreting the study findings. First, the definition of financial contagion is not universally accepted, and the study results are based on the specific definition and methodology. Second, the matching of daily financial market and BTC data with annual technological and regulatory variable data may have limited the strength of the study findings. However, the authors’ use of both parametric and nonparametric methods provides insights that may inspire further research into cryptocurrency markets and financial contagions. Practical implications Based on the authors analysis, they suggest that financial market regulators prioritize the development and adoption of new technologies and international accounting standard practices, rather than focusing solely on the potential risks associated with cryptocurrencies. While a cryptocurrency crash could harm individual investors, it is unlikely to pose a significant threat to the overall financial system. Originality/value To the best of the authors knowledge, they have not found an asset pricing approach to assess a possible contagion. The authors have developed a new method to evaluate whether there is a contagion from BTC to financial markets. A simple but intuitive asset pricing method to evaluate a systematic risk from a factor is a single index model. The single index model has been extensively used in stock markets but has not been used to evaluate the systemic risk potentials of cryptocurrencies. The authors followed Morck et al. (2000) and Durnev et al . (2004) to assess whether there is a systemic risk from BTC to financial markets. If the BTC possesses a systematic risk, the explanatory power of the BTC index model should be high. Therefore, the first implied contribution is to re-evaluate the findings from Aslanidis et al. (2019), Dahir et al . (2019) and Handika et al . (2019), using a different method.
Jéfferson Augusto Colombo, Tanzina Akhter, Peter Wänke, Md. Abul Kalam Azad · 7 authors
In the rapidly evolving domain of digital finance, the interplay between cryptocurrencies and external variables such as financial and social media indicators warrants thorough examination. This investigation employs a novel, entropy-weighted Multiple Attribute Decision Making (MADM) model to decipher these intricate relationships. The study's foundation is an expansive dataset, meticulously compiled to encompass a broad spectrum of financial data alongside diverse social media indicators. Central to this analysis is the employment of the Stepwise Weight Assessment Ratio Analysis (SWARA) method, meticulously applied to ascertain the relative importance of various social media indicators. Complementing this, the Complex Proportional Assessment (COPRAS) methodology is adeptly utilized to derive utility functions for each cryptocurrency under scrutiny. The analytical prowess of neural network regressions is harnessed to delineate the influence exerted by a multitude of financial indicators on these utility functions. The findings of this research are pivotal in understanding the dynamics within the cryptocurrency market. Bitcoin and Ripple emerge as pivotal entities, primarily functioning as primary conduits for market shocks. In contrast, Ethereum is identified as a stabilizing force, predominantly absorbing such fluctuations. A nuanced aspect of this study is the differential impact of social media indicators on various cryptocurrencies. Bitcoin and Ethereum display a negative correlation with these indicators, suggesting a complex, possibly inverse relationship with social media dynamics. Conversely, Litecoin, Dogecoin, and Ripple exhibit a positive responsiveness, indicating a heightened susceptibility to social media attention, sentiment, and prevailing uncertainty.
Non-Fungible Tokens (NFTs) are a developing area in the market of digital assets. NFTs represent digital or real-world items like artwork, gaming collectibles and real estate. We aim to study the daily working of NFT market and its interaction with cryptocurrency (Ether and Bitcoin) and search interest.Our approach involves identification of models encompassing both global and local feature importance. Various regression methods are utilized to determine the feature importance and select the predictive features effectively. Moreover, this study explores the relationship between search interest and weekly NFT sales and vice versa, to comprehend how public interest impacts the NFT market. Lastly, anomalies in daily sales are detected and analysed using STL Decomposition and SHAPely.The study reveals that intrinsic sales attributes and trade profits drive daily NFT sales, with positive sentiment significantly impacting Ethereum volatility and NFT sales. External factors like NFT supply, Ether price, and trade profits also influence anomalies. Positive sentiment significantly shapes crypto and NFT market dynamics.
This study aims to identify a secure and efficient trading approach for investors in highly volatile cryptocurrency markets. While pairs trading is a promising strategy, the available literature on this topic in cryptocurrency markets is limited and primarily based on traditional methods. This study compares six statistical approaches for selecting trading pairs: Cointegration, Correlation, Distance, Fluctuation Behaviour, Hurst Exponent, and Stochastic Differential Residual. We utilise intraday data from 30 cryptocurrencies on the Binance exchange during a three-month period from 1 January 2022 to 31 March 2022. The trading results are measured by 11 criteria covering return, risk, and trade aspects. The findings show that Distance performs well at all three frequencies of 1 minute, 5 minutes, and 60 minutes, with a total return of 208.12%, 236.31%, and 210.36%, respectively. At the 60-minute frequency, this method exhibits low risk and performs impressively even when all other techniques generate negative returns, as well as displays the lowest number of expired counts and the highest success ratio among the methods. At 1-minute and 5-minute frequencies, Cointegration and Hurst Exponent produce results comparable to those of Distance. The Student t-test conducted confirms these conclusions.
The valuation of the cryptocurrency market surpassed three trillion dollars in 2022, underscoring the burgeoning interest in digital currencies and decentralized finance.In response, on June 7, 2022, a bipartisan initiative led to the introduction of the "Responsible Financial Innovation Act," positioning cryptocurrencies as commodities and designating the Commodity Futures Trading Commission as the primary regulatory authority for the cryptocurrency market.Intriguingly, a study by Kim et al. (2022, JABE & IJBR) utilized a non-Euclidean methodology, suggesting that cryptocurrencies, in terms of their price dynamics, resemble securities more than commodities.However, a critical assessment of Kim et al. (2022, IJBR) reveals a methodological gap: the non-Euclidean distances were employed to derive a Euclidean configuration of 28 asset classes via multi-dimensional scaling.This Euclidean structure was subsequently employed for asset class categorization using -means clustering.This approach, while acknowledging the non-Euclidean distances among the 28 asset classes, leverages a Euclidean embedding for classification.In contrast, our research employs data depth to categorize asset classes without resorting to Euclidean embedding.We compare our findings with those of Kim et al. (2022, JABE & IJBR) for a comprehensive understanding.
Cryptocurrency Regulation in a Robust Market - The Vietnamese Approach The Vietnamese market has consistently demonstrated impressive levels of grassroots adoption of cryptocurrencies and, therefore, is always a prospective market for cryptocurrency development. This is one of the driving factors behind the need for effective cryptocurrency regulation in the country, so that investors and consumers are properly protected. However, as is the case with all new elements of the economy, regulating cryptocurrencies is a delicate process of balancing between economic development and protection of parties with limited leverage. If laws are too strict, then the market momentum is stifled and an economic opportunity is missed. Conversely, if laws are too lax, then the market is poorly regulated and fraud may become widespread and cause severe damages. This article aims to demonstrate the overall Vietnamese position on cryptocurrency regulation, including how it is being executed and how it might be executed in the foreseeable future.
Georgiana Iulia LAZEA, Ovidiu-Constantin Bunget, Anca Diana SUMANARU
This article aims to provide a comparative analysis of cryptocurrencies and fiat money, in the context in which the former might be considered an alternative to the latter. Mainly, we perform a literature review and qualitative analysis of 64 articles from Web of Science Core Collection, published between 2017 and 2023, using as keywords “cryptocurrencies” and “fiat money”. The information processing methodology involved presenting the data and information concisely, in order to gain a point of view on how crypto assets can be perceived in comparison with other financial assets. The results present the authors’ conclusions regarding the economic differences and similarities between cryptocurrencies and traditional money. It also includes the limitations of the research and offers future directions for study.
Frequent price manipulation in the Bitcoin market will lead to market risk and seriously disrupt the financial order, but there is less research on its regulation. We address the Bitcoin price manipulation problem by building a regulatory game model. First, we study the price manipulation mechanism of the Bitcoin market based on behavioral finance and clarify the boundary conditions. Second, we introduce regulator constraints and establish a game model between the manipulator and the regulator. Further, through variable deconstruction, parameter verification, and simulation analysis, we explore how to achieve effective regulation of Bitcoin price manipulation. We find that the effective regulation of Bitcoin price manipulation can be achieved in three ways: (1) Adjust the penalty coefficient with a certain lower threshold so that the manipulator's expected return is negative; (2) Set the lowest possible price fluctuation standard while ensuring that it does not interfere with market-based transactions; (3) The simulation of price manipulation regulation is optimized and most efficiently controlled when the probability of investigation is dynamically adjusted by a concave function on the price fluctuation standard.