This paper examines the effect of investor attention on the cross-section of cryptocurrency returns and trading activities. We find that cryptocurrencies associated with higher abnormal Google search volume subsequently exhibit higher returns, higher volatility, and higher trading volume. The results are robust to alternative sample periods and alternative search keywords, providing concrete support to the attention-induced price pressure hypothesis and consistent with prior studies on the equity market. The effect is more pronounced among larger cryptocurrencies. Only a partial reversal after the initial return increase is observed, implying that investor attention permanently impacts cryptocurrency prices.
This work aims to contribute to a deeper understanding of cryptocurrencies, which have emerged as a unique form within the financial market. While there are numerous cryptocurrencies available, most individuals are only familiar with Bitcoin. This knowledge gap and the lack of literature on the subject motivated the present study to shed light on the key characteristics of cryptocurrencies, along with their advantages and disadvantages. Additionally, we seek to investigate the integration of cryptocurrencies within the financial market by applying a dynamic equicorrelation model. The analysis covers ten cryptocurrencies from June 2nd, 2016 to May 25th, 2021. Through the implementation of the dynamic equicorrelation model, we have reached the conclusion that the degree of integration among cryptocurrencies primarily depends on factors such as trading volume, global stock index performance, energy price fluctuations, gold price movements, financial stress index levels, and the index of US implied volatility.
Muhammad Mahmudul Karim, Mohamed Eskandar Shah Mohd Rasid, Abu Hanifa Md. Noman, Larisa Yarovaya
This paper aims to analyze the return-volatility relationship of Bitcoin and Ethereum across different return frequencies and all conditional quantiles of implied volatility, based on a unique 6.5 million observations. We employ the newly constructed Model-Free Implied Volatility (MFIV) of Bitcoin (BitVol) and Ethereum (EthVol) and use an asymmetric Quantile Regression Model (QRM) to capture the intraday asymmetric return-volatility relationship at different quantiles of the distribution of the dependent variable. Our findings show that the estimated coefficient using daily data is significant only at medium- to high-volatility regimes, while the estimated coefficients using high-frequency data are highly significant across all volatility regimes. Moreover, our results indicate that the asymmetry varies across frequencies and quantiles, with weak asymmetric effects at low quantiles and high frequencies, and strong asymmetric effects at high quantiles and low frequencies. This study provides new insight, especially for high-frequency traders. ⢠We analyze 6.5 million observations to unveil intraday asymmetric return-volatility dynamics in Bitcoin and Ethereum. ⢠The Model-Free Implied Volatility, Quantile Regression Model, and Wavelet Coherence are employed. ⢠We found that asymmetry in these relationships intensifies at lower frequencies and high quantiles. ⢠Findings contribute to cryptocurrency literature using high-frequency data across different intervals.
Mar Grande, F. Borondo, Juan Carlos Losada, J. Borondo
Pairs trading is a short-term speculation trading strategy based on matching a long position with a short position in two assets in the hope that their prices will return to their historical equilibrium. In this paper, we focus on identifying opportunities where mean reversion will happen quickly, as the commission costs associated with keeping the positions open for an extended period of time can eliminate excess returns. To this end, we propose the use of the local Hurst exponent as a signal to open trades in the cryptocurrencies market. We conduct a natural experiment to show that the spread of pairs with anti-persistent values of Hurst revert to their mean significantly faster. Next, we verify that this effect is universal across pairs with different levels of co-movement. Finally, we back-test several pairs trading strategies that include H<0.5 as an indicator and check that all of them result in profits. Hence, we conclude that the Hurst exponent represents a meaningful indicator to detect pairs trading opportunities in the cryptocurrencies market.
Rasoul Amirzadeh, Dhananjay Thiruvady, Asef Nazari, Mong Shan Ee
Abstract Understanding the relationships between cryptocurrencies is important for making informed investment decisions in this financial market. Our study utilises Bayesian networks to examine the causal interrelationships among six major cryptocurrencies: Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether. Beyond understanding the connectedness, we also investigate whether these relationships evolve over time. This understanding is crucial for developing profitable investment strategies and forecasting methods. Therefore, we introduce an approach to investigate the dynamic nature of these relationships. Our observations reveal that Tether, a stablecoin, behaves distinctly compared to mining-based cryptocurrencies and stands isolated from the others. Furthermore, our findings indicate that Bitcoin and Ethereum significantly influence the price fluctuations of the other coins, except for Tether. This highlights their key roles in the cryptocurrency ecosystem. Additionally, we conduct diagnostic analyses on constructed Bayesian networks, emphasising that cryptocurrencies generally follow the same market direction as extra evidence for interconnectedness. Moreover, our approach reveals the dynamic and evolving nature of these relationships over time, offering insights into the ever-changing dynamics of the cryptocurrency market.
The utilization of machine learning techniques for predicting cryptocurrency prices has become increasingly prominent. Researchers have investigated a variety of methods, including recurrent neural networks, deep learning architectures, Bayesian regression, k-nearest neighbors, and support vector machines, to forecast prices for cryptocurrencies such as Bitcoin, Ethereum, Dogecoin, and Litecoin, etcetera. This research draws from existing studies on price prediction across different domains, including the predictability of sales, fluctuations of sale prices, gold price forecasting, and silver price predictions. The focus has been on exploiting high-dimensional features and time-series analysis while comparing various statistical and machine learning models. Models have also incorporated factors such as market liquidity and exchange dynamics. Although current literature acknowledges the potential of these methods in predicting cryptocurrency, gold and silver, there is a noted gap in applying these techniques to a wider range of cryptocurrencies. Crypto-Visionary will integrate a variety of machine learning and statistical techniques to forecast prices for cryptocurrencies, gold and silver, considering factors like market trends, trading networks, and visual attributes. Additionally, the importance of feature engineering and sample dimension manipulation is emphasized to improve the accuracy and reliability of predictions. As the cryptocurrency market evolves, there is a growing need for further research to develop robust models capable of forecasting prices for a diverse set of cryptocurrencies, thereby advancing the field.
The study investigates the nonlinear contagion, tail dependence, and Granger causality relations with TAR-TR-GARCHâcopula causality methods for daily Bitcoin, Fintech, energy consumption, and CO2 emissions in addition to examining these series for entropy, long-range dependence, fractionality, complexity, chaos, and nonlinearity with a dataset spanning from 25 June 2012 to 22 June 2024. Empirical results from Shannon, RĂŠnyi, and Tsallis entropy measures; KolmogorovâSinai complexity; HurstâMandelbrot and Loâs R/S tests; and Phillipsâ and Geweke and Porter-Hudakâs fractionality tests confirm the presence of entropy, complexity, fractionality, and long-range dependence. Further, the largest Lyapunov exponents and Hurst exponents confirm chaos across all series. The BDS test confirms nonlinearity, and ARCH-type heteroskedasticity test results support the basis for the use of novel TAR-TR-GARCHâcopula causality. The model estimation results indicate moderate to strong levels of positive and asymmetric tail dependence and contagion under distinct regimes. The novel method captures nonlinear causality dynamics from Bitcoin and Fintech to energy consumption and CO2 emissions as well as causality from energy consumption to CO2 emissions and bidirectional feedback between Bitcoin and Fintech. These findings underscore the need to take the chaotic and complex dynamics seriously in policy and decision formulation and the necessity of eco-friendly technologies for Bitcoin and Fintech.
We analyze the token transfer network on Ethereum, focusing on accounts associated with Alameda Research, a cryptocurrency trading firm implicated in the misuse of FTX customer funds. Using a multi-token network representation, we examine node centralities and the network backbone to identify critical accounts, tokens, and activity groups. The temporal evolution of Alameda accounts reveals shifts in token accumulation and distribution patterns leading up to its bankruptcy in November 2022. Through network analysis, our work offers insights into the activities and dynamics that shape the DeFi ecosystem.
Mustafa KoçoÄlu, XuanâHoa Nghiem, Ehsan Nikbakht
Purpose In this study, we aim to investigate the connectedness spillovers among major cryptocurrency markets. Moreover, we also explore to identify factors driving this connectedness, particularly focusing on the sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies under Twitter-based economic uncertainties and US economic policy uncertainty. Finally, we investigate the extent to which cryptocurrency markets serve as a safe haven, hedge, and diversifier from news-based uncertainties. Design/methodology/approach This study employs the connectedness approach following the combination of Ando et al . (2022) QVAR and BarunĂk and KrehlĂk's (2018) frequency connectedness methodologies into the framework proposed by Diebold and Yilmaz (2012, 2014). The data covered from November 10, 2017, to April 21, 2023, and the factors driving cryptocurrency connectedness spillovers are identified and examined. The sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies, concerning Twitter-based economic uncertainties and US economic policy uncertainty, are analyzed. We apply the Wavelet quantile correlation (WQC) method developed by Kumar and Padakandla (2022) to explore the effects of Twitter-based economic uncertainties and US economic policy uncertainty on Cryptocurrency market connectedness risk spillovers. Besides, we check and present the robustness of WQC findings with the multivariate stochastic volatility method. Findings Our findings indicate that Ethereum and Bitcoin are net shock transmitters at the center of the connectedness return network. Ethereum and Bitcoin hold the highest market capitalization and value in the cryptocurrency market, respectively. This suggests that return shocks originating from these two cryptocurrencies have the most significant impact on other cryptocurrencies. Tether and Monero are the net receivers of return shocks, while Cardano and XRP exhibit weak shock-transmitting characteristics through returns. In terms of return spillovers, Ethereum is the most effective, followed by Bitcoin and Stellar. Further analysis reveals that Twitter economic policy uncertainty and US economic policy uncertainty are effective drivers of short-term and total directional spillovers. These uncertainty indices exhibit positive coefficient signs in short-term and total directional spillovers, which turn predominantly negative in different magnitudes and frequency ranges in the long term. In addition, we also document that as the Total Connectedness Index (TCI) value increases, market risk also rises. Also, our empirical findings provide significant evidence of Twitter-based economic uncertainties and US economic policy uncertainty that affect short-term market risks. Hence, we state that risk-connectedness spillovers in cryptocurrency markets enclose permanent or temporary shock variations. Besides, findings of the low value of long-term spillovers suggest that risk shocks in cryptocurrency markets are not permanent, indicating long-term changes require careful monitoring and control over market dynamics. Practical implications In this study, we find evidence that Twitter's news-based uncertainty and US economic policy uncertainty have a significant effect on short-term market risk spillovers. Furthermore, we observe that high cryptocurrency market risk spillovers coincide with periods of events such as the US-China trade tensions in January 2018, the Brexit process in February 2019, and the COVID-19 outbreak in November 2019. Next, we observe a decline in cryptocurrency market risk spillovers after March 2020. The reason for this mitigation of market risk spillover may be that the Fed's quantitative easing signals have initiated a relaxation process in the markets. Because the Fed's signal to fight inflation in March 2022 also coincides with the period when risk spillover increased in crypto markets. Based on this, we present evidence that the FED's communication mechanism with the markets can potentially affect both short- and long-term expectations. In this context, we can say that our hypothesis that uncertainty about the news causes short-term risks to increase has been confirmed. Our findings may have investment policy implications for portfolio managers and investors generally in terms of reducing financial risks. Originality/value Our paper contributes to the literature by examining the interconnectedness among major cryptocurrencies and the drivers behind them, particularly focusing on the role of news-based economic uncertainties. More broadly, we calculate the utilization of advanced methodologies and the incorporation of real-time economic uncertainty data to enhance the originality and value of the research, which provides insights into the dynamics of cryptocurrency markets.
This study employs event study methodology to investigate the impact of various types of events on cryptocurrency market returns and volatility. The research focuses on six major cryptocurrenciesâETH, BTC, BNB, XRP, DOGE, and TRXâover the period from December 31, 2017, to October 30, 2023. Six types of events are analyzed: cybersecurity events, block reward adjustment events, political conflict events, public health emergency events, cryptocurrency recognition and support events, and social media sentiment events. The findings reveal that cybersecurity and block reward adjustment events have minimal and short-lived impacts on market returns. Political conflict events cause significant short-term return volatility depending on market expectations. Public health emergency events, such as the COVID-19 pandemic, have significant and lasting negative impacts on market returns. Cryptocurrency recognition and support events have significant and sustained positive impacts on market returns. Social media sentiment events have significant but short-lived impacts on market returns. The robustness of the results was validated through the analysis of abnormal returns during the event period. This study provides valuable insights for investors and policymakers in managing market volatility.
Abstract Systematic risks in cryptocurrency markets have recently increased and have been gaining a rising number of connections with economics and financial markets; however, in this area, climate shocks could be a new kind of impact factor. In this paper, a spillover network based on a time-varying parametric-vector autoregressive (TVP-VAR) model is constructed to measure overall cryptocurrency market extreme risks. Based on this, a second spillover network is proposed to assess the intensity of risk spillovers between extreme risks of cryptocurrency markets and uncertainties in climate conditions, economic policy, and global financial markets. The results show that extreme risks in cryptocurrency markets are highly sensitive to climate shocks, whereas uncertainties in the global financial market are the main transmitters. Dynamically, each spillover network is highly sensitive to emergent global extreme events, with a surge in overall risk exposure and risk spillovers between submarkets. Full consideration of overall market connectivity, including climate shocks, will provide a solid foundation for risk management in cryptocurrency markets.
The financial markets experienced a thrilling saga between 2020 and 2023, characterised by a series of unprecedented events and captivating dynamics that set the stage for a compelling exploration of the interaction between bitcoin prices and the S&P 500 Index. This study systematically examines the correlation between bitcoin prices and the S&P 500 Index using the Yahoo Finance dataset over a 48-month period. Using the extensive Yahoo Finance dataset and the analytical capabilities of R Statistics & R Studio, the present research covers a comprehensive period of 48 months (2020-2023). The study identifies a robust positive correlation, quantified by a correlation coefficient of 0.7726, indicating a significant alignment between bitcoin price movements and the S&P 500 index. Monthly price variables obtained from an open-source repository provide a comprehensive overview of the relative dynamics of these financial assets. This analysis provides valuable insights into the current behaviour of bitcoin and the S&P 500 index, as well as concise observations on the dynamics of their correlation.
Roland Akuoko-Sarpong, Stephen Tawiah Gyasi, Hannah Affram
The creation of cryptocurrencies has signified many consequences for financial markets of the traditional kind and their effectiveness. This research seeks to explore the effects of cryptocurrencies on a number of the other traditional markets in aspects of price discovery, volatility, interdependence, and information transmission. Event study analysis of everyday price changes and using multivariate cointegration analysis to cryptocurrencies and the evidence is that the cryptocurrencies are inefficient as characterized by irrational behavior, bubbles, and erratically fluctuating volatilities. However, they affect a range of currency, commodity, and stock market indexes by showing return and volatility spillover effects suggesting information flowing from one market to another. Alnet, cryptocurrency markets seem inefficient on their own but over time enhance the efficiency of linked traditional markets through participation and connectivity of global financial systems. The study contributes valuable insights into the evolving nature of financial markets in the digital era through discussions on market structure, behavioral factors, and policy implications.
Asim Ghosh, Soumyajyoti Biswas, Bikas K. Chakrabarti
We study the fluctuations, particularly the inequality of fluctuations, in cryptocurrency prices over the last ten years. We calculate the inequality in the price fluctuations through different measures, such as the Gini and Kolkata indices, and also the $Q$ factor (given by the ratio between the highest value and the average value) of these fluctuations. We compare the results with the equivalent quantities in some of the more prominent national currencies and see that while the fluctuations (or inequalities in such fluctuations) for cryptocurrencies were initially significantly higher than national currencies, over time the fluctuation levels of cryptocurrencies tend towards the levels characteristic of national currencies. We also compare similar quantities for a few prominent stock prices.
In the past years, the widespread diffusion of Artificial Intelligence (AI) in the finance domain transformed different services, with particular attention to the stock market. Although different AI-based approaches have been proposed for stock forecasting, they are focused on news content or sentiment without considering fundamental features and vice versa. In turn, other approaches rely on handmade rules or ones based on technical indicators for providing advice without considering contextual information that can strongly affect the stock market. In this paper, we propose an Advisor Neural Network framework using Long Short-Term Memory (LSTM)-based Informative Stock Analysis for Daily investment Advice. Specifically, the forecasting unit relies on a LSTM-based model, which combines technical indicators, contextual information, and financial data for stock forecasting. Successively, the advice unit provides next-day advice based on predicted information in conjunction with the proposed Heuristic Stocks Selection algorithm. This framework has been evaluated on the Stock and Cryptocurrencies markets, considering a subset of 417 stocks and 67 cryptocurrencies over three years, respectively. We compared the proposed framework with several state-of-the-art approaches, showing how it outperforms the baseline in both markets. Furthermore, we achieved a financial gain greater than 41%, despite the downward trend of the NASDAQ market in the quarter under review, and we obtained a 39.38% return on investment for the Cryptocurrencies market.
Abstract This paper investigates the dynamic relationships between the volatility of Bitcoin and major Indian stock market indices. Employing a dynamic conditional correlationâgeneralized autoregressive conditional heteroskedasticity (DCCâGARCH) model, we explore how volatility shocks and information flow influence the correlations between these asset classes. Our findings reveal a key characteristic: volatility spillovers tend to be shortâlived, indicated by a relatively low DCCâGARCH parameter (dcca1). This suggests that while a surge in volatility in one market might lead to a temporary increase in correlation with the other, this heightened correlation is unlikely to persist for extended periods. However, the model also highlights a high DCCâGARCH parameter (dccb1), signifying that the correlations themselves are responsive to new information. This implies that volatility linkages can adjust rapidly in response to market events or economic data releases. To enhance accessibility for a broad audience, we translate these findings into economic intuitions. We illustrate how the model can be interpreted through realâworld examples, such as the impact of sudden policy changes in India or global market flash crashes. By understanding the shortâlived nature of volatility spillovers and the responsiveness of correlations, investors in the Indian markets can make more informed decisions when considering the potential influence of Bitcoin's volatility while contributing to a deeper understanding of the dynamic interactions between cryptocurrency and traditional financial markets in the Indian context.
Arfan Shahzad, Yasmin Anwar, Muhammad Arif Nadeem, Waqas Shair
The advancement in technologies has changed the picture of todayâs economy. Cryptocurrency is the most trending currency nowadays. The form of cryptocurrency that is most commonly used in trading is Bitcoin. Since 2016, continuous fluctuations have been observed in the price of Bitcoin. The objective of the current study is to classify the strong predictor of Bitcoinâs price fluctuations and the associations of all these variables with each other. The price of several variables is selected as independent variables, including oil, VIX index, and US dollars. The price values for all study variables are collected for one year daily. The study findings indicated that lag 2 in the VAR model is the optimum lag for the model using HQIC and SBIC criteria, so todayâs price depends on the previous two daysâ price of independent variables. The correlation results indicated that the previous two-day price of EURO predicts the BTCâs todayâs price. A negative association is found between VIX and BTC. It is indicated that a 1 percent increase in the price of the VIX index will lead to the 60 decreases in BTCâs today price. The study also showed that it is not the price of BTC that forecasts todayâs worth of BTC, but it is the prices of VIX, euro, and oil that can predict todayâs price of BTC.
This article investigates the time-series properties of cryptocurrency returns and compares them with currency and commodity returns. We perform and analyze the mean reversion, normality, unit root, high and low returns, correlation, Autoregressive Moving Average (ARMA) [2,2], Autoregressive (AR) [5], and long-run components in the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) [1,1] estimates. We also perform regression analyses to evaluate two possible behavioral biases: familiarity and disposition effect. Our time series analysis documents that cryptocurrencies are neither currencies nor commodities. We also show that adding cryptocurrency to a portfolio increases market efficiency and uncertainty. We also document that cryptocurrency investors exhibit the same familiarity and disposition effect biases as commodity and currency investors. Overall, we conclude that investors in cryptocurrencies tend to underestimate risk and misestimate future prices, as they do in commodity and currency markets. This study makes at least three contributions to the literature. First, we evaluate whether cryptocurrencies tend to hedge or financialization. Second, our analysis includes both univariate and portfolio dimensions. Third, this is a pioneering study on using behavioral bias analysis to determine whether a cryptocurrency is a commodity or a currency.
We analyzed Bitcoinâs cyclical patterns used by the Markov regime-switching model and explored the impacts of inflation and the US Dollar Index on Bitcoinâs cyclicality. The results showed Bitcoinâs cyclical pattern, the effects of the US dollar index and VIX on Bitcoinâs cyclical pattern, and how the US dollar index and VIX affect BTCâs structural changes in Bitcoin.