Cryptocurrency Returns Research Articles

Extremes of extremes: risk assessment for very small samples with an exemplary application for cryptocurrency returns

Extremes of extremes: risk assessment for very small samples with an exemplary application for cryptocurrency returns

Financial Uncertainty and the Cross-Section of Cryptocurrency Returns

Financial Uncertainty and the Cross-Section of Cryptocurrency Returns

The Impact of Volatility, Liquidity, and Crude Oil Price Index on Cryptocurrency Returns (Testing the Weak-Form Market Efficiency) Period January 2019 - June 2021

Since it was first founded by Satoshi Nakamoto in 2008, cryptocurrencies have attracted the attention of investors significantly. Until now, many investors have invested their money in cryptocurrencies. This study aims to prove and analyze the effect of volatility, liquidity and world oil price indices on cryptocurrency returns. It also examines whether the occurrence of market efficiency is weak or not. This study aims to prove and analyze weak efficiency market and the impact of liquidity, volatility and the world oil price index on the return of the cryptocurrency market. This study uses quantitative methods and secondary data, so that the number of samples taken are 3 types of cryptocurrencies (Bitcoin, Ethereum and Binance Coin) which are listed on coinmarketcap.com and investing.com, as well as the oil price index taken from investing.com The analytical methods used in this study are series correlation test and runs test as well as multiple linear regression analysis. The results of this study indicate that volatility and liquidity affect the efficiency of the cryptocurrency market while the oil price index has no effect. The results of autocorrelation test on return itself prove that these coins have weak form efficiency. Keywords: Market Efficiency, Cryptocurrency, Liquidity, Oil Price Index, Volatility

Portfolio Diversification, Hedge and Safe-Haven Properties in Cryptocurrency Investments and Financial Economics: A Systematic Literature Review

Our study collected and synthetized the existing knowledge on portfolio diversification, hedge, and safe-haven properties in cryptocurrency investments. We sampled 146 studies published in journals ranked in the Association of Business Schools 2021 journals list, considering all fields of knowledge, and elaborated a systematic literature review along with a bibliometric analysis. Our results indicate a fast-growing literature evidencing cryptocurrencies’ ability to hedge against stocks, fiat currencies, geopolitical risks, and Economic Policy Uncertainty (EPU) risk; also, that cryptocurrencies present diversification and safe-haven properties; that stablecoins reveal unstable peg with the US dollar; that uncertainty is a determinant for cryptocurrency returns. Additionally, we show that investors should consider Gold, along with the European carbon market, CBOE Bitcoin futures, and crude oil to hedge against unexpected movements in the cryptocurrency market.

Do Stock Market Volatility and Cybercrime Affect Cryptocurrency Returns? Evidence from South African Economy

The study investigates the effects of stock market volatility and cybercrime on cryptocurrency returns in the South African economy. Daily time series data on four different types of cryptocurrencies (Bitcoin, Ethereum, Tether, and BMB) were employed. The data covers the period from 1 January 2019–31 December 2021. The study employed the dynamic conditional correlation (DCC GARCH) and Bayesian liner regression model to investigate time-varying correlations among the variables. Empirical findings suggest that stock market volatility has a positive impact on the returns of BNB, Bitcoin, and Ethereum. However, it has a negative impact on Tether. Expectedly, cybercrime poses negative impacts on the returns of BNB, Bitcoin, and Ethereum but could be said to have no impact on the returns of Tether. The study concludes that ongoing efforts to reduce cybercrime activities need to be strengthened to further the use of digital currencies.

Safe-Haven Effectiveness of Cryptocurrency: Evidence from Stock Markets of COVID-19 Worst-Hit African Countries

The study assessed the hedge or safe-haven property of five cryptocurrencies for stocks of three COVID-19 worst-hit African countries. We address two main concerns bordering on the predictive capacity of African stocks for cryptocurrency returns and the safe-haven property that cryptocurrencies could offer to African stocks. A distributed lag model, with explicitly incorporated salient statistical features, was adopted based on its efficient management of parameter proliferation and estimation biases. We ascertained the model’s in-sample predictability and evaluate its out-of-sample forecasts performance in comparison with the historical average model, using Clark and West statistics. While African stocks significantly predicted cryptocurrency returns, the cryptocurrency-stocks nexus revealed the diversifier and safe-haven property of cryptocurrencies for African stocks in periods of normalcy and crisis/pandemic, respectively. Our predictive model outperformed the historical average model in the out-of-sample. Our results may be sensitive to cryptocurrency-stocks nexus and sample periods but not the out-of-sample forecast horizons

An Analysis of the U.S. Individual Investor Sentiment Influence on Cryptocurrency Returns and Volatility

In this research, the U.S. investor sentiment effect on cryptocurrency returns and volatility is examined by separating it into irrational and rational parts. According to the data, an unforeseen rise in the rational part of U.S. individual investor attitude influences cryptocurrency returns statistically and positively. In other words, rational sentiment can result in rising cryptocurrency returns. Additionally, a positive significant association exists between cryptocurrency volatility and the rational part of the individual U.S. investor sentiment. The findings confirm the hypothesis that the behavior of rational investors who utilize and study the impact of economic factors on asset prices reduces cryptocurrency volatility.

Cryptocurrency Returns, Cybercrime and Stock Market Volatility: GAS and Regime Switching Approaches

This study examines the behaviour of cryptocurrencies’ returns to stock market volatility and cybercrime in the South African economy. The study makes us Generalized Autoregressive Score Model (GAS) investigate the time-varying correlation between cryptocurrencies’ returns and cybercrime, and cryptocurrencies’ returns and stock market volatility by making use of daily time series data on different four types of Cryptocurrencies, Bitcoin, Ethereum, Tether and BNB from January 2019 to December 2021. The study also makes use of the regime-switching approach to regime-switching impacts on the cryptocurrencies’ returns. The empirical results obtained showed that cybercrime, on average, has negative impacts on the cryptocurrencies’ returns and the time-varying correlation between stock market volatility and each of the cryptocurrencies’ returns is largely positive. The stock market volatility impact is found to be regime-switching dependent. The study recommends that efforts to reduce cybercrime activities must be reinforced to deepen the use of digital currencies and policy measures must be taken to ensure reduced or moderate stock market volatility.

COVID-19 and Cryptocurrency Market: Impact on Return, Volatility and Liquidity

In this study, we examine the impact of COVID-19 pandemic on the return, volatility and liquidity of the cryptocurrencies using panel data analysis. We consider the four variables related to the COVID-19 pandemic: (i) percentage change in total confirmed cases, (ii) percentage change in total recovered cases, (iii) percentage change in total death cases, and (iv) the investor’s attention towards the COVID-19 pandemic. The findings indicate that percentage change in recovered cases and investor attention towards COVID-19 have a significant positive impact on returns of cryptocurrencies. The volatility in cryptocurrencies is negatively influenced by the percentage change in recovered cases. Moreover, the percentage change in recovered cases has a significant negative impact and investor attention towards COVID-19 has a significant positive impact on the liquidity of cryptocurrencies.

Assessing the resiliency of investors against cryptocurrency market crashes through the leverage effect

By analyzing a large cross-section of cryptocurrencies, we document the absence of the leverage effect in this market. Unlike the equity market, investors exhibit less panicking behavior and appear indifferent to negative returns in terms of market participation. Moreover, the negative asymmetric effect is reverted for some cryptocurrencies in our dataset, showing the investors’ fear of missing out. Our results are robust over different leverage effect models and historical time windows. • Our analysis reveals a negative asymmetric effect on daily cryptocurrency returns. • A comparable cross-section of stocks exhibits a more significant leverage effect. • Cryptocurrency investors exhibit less panicking behavior due to the FOMO effect. • The analysis is robust in the leverage models and the time windows considered.

Dynamic analysis of calendar anomalies in cryptocurrency markets: evidences of adaptive market hypothesis

ABSTRACT This paper analyses the effects known as the day of the week and the month of the year in the cryptocurrency markets. The closing values of eleven cryptocurrencies have been considered. The study employs dummy variable regression techniques, ANOVA and Friedman tests for assessing two calendar anomalies, the day-of-week and month-of-year effects. To test these calendar effects, we have applied both full sample and rolling-regression techniques for two lengths of the rolling sample intervals. Furthermore, we have examined the existence of long memory in day-of-the- week and month-of-the-year cryptocurrency returns. The results provide evidence about the existence of day-of-the-week and month-of-the-year effects in cryptocurrency returns, in particular, on Thursdays and in November. In addition, it should be added that the general results of the current study show that the calendar effect in the cryptocurrency market is dynamic rather than static, which indicates that the calendar effect is a phenomenon that varies over time.

Retail vs institutional investor attention in the cryptocurrency market

We investigate the impact of retail vs institutional investor attention on returns, idiosyncratic risk and liquidity of the cryptocurrency market. Accordingly, retail (institutional) investor attention has a negative (positive) effect on cryptocurrency returns. Moreover, retail (institutional) investor attention aggravates (constrains) the idiosyncratic risk whereas both type of attention boost liquidity of the cryptocurrency market. However, only retail investor attention exacerbates idiosyncratic volatility in unstable market conditions whereas it has a constructive effect on liquidity in low global economic policy uncertainty. Furthermore, institutional investor attention has a constructive impact on both idiosyncratic risk and liquidity within relatively stable and rising external market environment.

Does the realized distribution-based measure dominate particular moments? Evidence from cryptocurrency markets

This study investigates the relationship between the daily returns of cryptocurrencies and their realized performance measure and moments (variance, skewness, and kurtosis). Because the cryptocurrency returns are non-Gaussian distributions and perform bubble-like behaviors, we adopt the performance measure proposed by Schnytzer and Westreich (2013) to capture all informational content related to the distribution of a return. First, the empirical results revealed that there is a positive relationship between the realized performance measure and daily returns. Furthermore, this realized performance measure dominates the realized variance, skewness, and kurtosis in terms of affecting and predicting Bitcoin and Ethereum daily returns.

Fat tails and network interlinkages of crude oil and cryptocurrency during the COVID-19 health crisis

PurposeThe authors attempt to explore fat tails and network interlinkages of oil prices and the six largest cryptocurrencies from 1st January 2018 and 1st August 2021. The authors also investigate the influences of the COVID-19 pandemic on these network interlinkages.Design/methodology/approachThe authors follow Diebold and Yilmaz (2012) to calculate the spillover index the dynamic correlation coefficient model firstly employed by Engle (2002) to study how the volatility of oil prices are transmitted to those of cryptocurrency return and liquidity and vice versa.FindingsThe results confirm the presence of time-varying interlinkages between the volatilities of the oil market and the cryptocurrency market. Notably, uncertain events like the COVID-19 health crisis significantly influence the time-varying interlinkages they augment dramatically during the COVID-19 health crisis. The turbulence of the cryptocurrency market, especially from Bitcoin and Ethereum, significantly impacts those of the oil market. The role of the oil market in transmitting the effect of respective shocks to the cryptocurrency market, on the other hand, is time-varying, which is only reported when the COVID-19 pandemic first appeared at the beginning of 2020. The turbulence of the cryptocurrency market in the system is greatly explained by themself rather than a transmission mechanism of shocks to the oil market.Practical implicationsInsightful knowledge about key antecedents of contagion among these markets also help policymakers design adequate policies to reduce these markets' vulnerabilities and minimize the spread of risk or uncertainty across these markets.Originality/valueThe most significant benefit of the approach is how simple it is to calculate net pairwise connectivity, which identifies transmission channels between these commodity and financial markets. The authors are also the first to use the quasi-maximum likelihood (QML) estimator to estimate the DCC model to measure the volatility spillover index to reflect the level of interdependence between the different markets. By using a daily and up to date database, the authors can observe the role of each market in transmitting and receiving the shocks between two different sub-periods: (1) before and (2) during the COVID-19 pandemic crisis.

Investor attention, Twitter uncertainty and cryptocurrency market amid the COVID-19 pandemic

PurposeThis paper aims to examine the impact of investor attention due to the COVID-19 pandemic, Twitter-based sentiment towards uncertainty and public sentiment on the performance of cryptocurrencies.Design/methodology/approachThe authors employ the simple linear regression, quantile regression (QR), the exponential generalised autoregressive conditional heteroskedasticity (EGARCH) model, and sentiment analysis to examine this phenomenon. The authors utilise the daily closing price of the 20 leading cryptocurrencies, the Google search volume index of the “Coronavirus” keyword, the Twitter-based economic uncertainty index, and textual data collected from the Reddit social media platform.FindingsThe results show that investor attention and Twitter uncertainty have a negative (positive) effect on cryptocurrency returns (volatility). The QR results indicate a heterogeneous effect of investor attention and Twitter economic uncertainty on cryptocurrency returns with a higher effect in the lower quantiles. The findings indicate that cryptocurrencies fail to act as a safe haven during this pandemic.Originality/valueThe study is amongst the very few studies that capture the impact of investor attention/sentiment due to COVID-19 on the performance of cryptocurrencies.

Sustainable versus Conventional Cryptocurrencies in the Face of Cryptocurrency Uncertainty Indices: An Analysis across Time and Scales

Are conventional and sustainable cryptocurrencies effective hedging instruments for high cryptocurrency uncertainty? This paper examines co-movements between conventional (Bitcoin, Ethereum, Binance Coin, Tether) and sustainable (Cardano, Powerledger, Stellar, Ripple) cryptocurrencies and two cryptocurrency uncertainty indices (UCRY price and UCRY policy). Using weekly returns from 1 October 2017 to 30 March 2021, the paper employs the bivariate wavelet coherence method considering three investment horizons, short-term, medium-term, and long-term. The results confirm that conventional and sustainable cryptocurrencies show consistent positive and identical co-movements with both cryptocurrency uncertainty indices at the short-term horizon during COVID-19 and negative co-movement at the medium-term investment horizon, suggesting the short-term hedging ability of dirty/green cryptocurrencies for high UCRY price and policy. Evidence of negative coherences shows that higher cryptocurrency prices and policy uncertainties lead to lower cryptocurrency returns, reflecting the adverse impact of higher uncertainties on the trust of crypto traders and investors. Weak co-movement is found between dirty/green cryptocurrencies and UCRY price/policy indices, which suggests the possible role of dirty/green cryptocurrencies as a weak hedge for UCRY price and policy indices. These findings provide potential avenues to hedge cryptocurrency uncertainties using conventional and sustainable cryptocurrencies across multiple investment horizons.

Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures

Portfolio risk management plays an important role in successful investments. Portfolio standard deviation, value-at-risk, expected shortfall, and maximum absolute deviation are widely used portfolio risk measures. However, the existing portfolio risk measures are vulnerable to larger skewness and kurtosis of the asset returns. Moreover, the traditional assumption of normality of the portfolio returns leads to the underestimation of portfolio risk. Cryptocurrencies are a decentralized digital medium of exchange. In contrast to physical money, cryptocurrency payments exist purely as digital entries on an online ledger called blockchain that describe specific transactions. Due to the high volume and high frequency of cryptocurrency transactions, risk forecasting using daily data is not enough, and a high-frequency analysis is required. High-frequency data reveal a very high excess kurtosis and skewness for returns of cryptocurrencies. In order to incorporate larger skewness and kurtosis of the cryptocurrencies, a data-driven portfolio risk measure is minimized to obtain the optimal portfolio weights. A recently proposed data-driven volatility forecasting approach with daily data are used to study risk forecasting for cryptocurrencies with high-frequency (hourly) big data. The paper emphasizes the superiority of portfolio selection of cryptocurrencies by minimizing the recently proposed risk measure over the traditional minimum variance portfolio.

Assessing causal relationships between cryptocurrencies and investor attention: New results from transfer entropy methodology

Studies apply non-parametric wavelet Granger causality testing to investigate bi-directional causalities of cryptocurrencies with Twitter and Google. However, this method only provides the existence of information flows without quantization and assumes time series are linear. Considering this, we highlight transfer entropy as an alternative, model-free methodology. We quantify the impact of search-engine attention (Google Trends) and social-media attention (Twitter) on cryptocurrency returns, employing in turn Shannon and Rényi transfer entropy methodologies. We document levels of bi-directional causalities, showing that tail events are more informative than center observations in the cryptocurrency market.

The Time-varying Impact of Covid-19 Pandemic on Niche Cryptocurrency Returns and Volatility

Since the outbreak of COVID-19 in 2020, all industries have been hit to a greater or lesser extent, and the financial sector is no exception. And in previous studies of mainstream cryptocurrencies, cryptocurrencies appear to have been relatively unaffected by the epidemic. This paper, however, explores the impact of the epidemic on niche cryptocurrencies such as BNB, ADA, and dogecoin based on VAR and ARMA-GARCH models. The results show that the impact on returns is relatively small, while the impact on volatility is almost non-existent. These results may provide references for government departments and individual investors to avoid the systemic risk caused by the epidemic.

Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment

The paper examines how various COVID-19 news sentiments differentially impact the behaviour of cryptocurrency returns. We used a nonlinear technique of transfer entropy to investigate the relationship between the top 30 cryptocurrencies by market capitalisation and COVID-19 news sentiment. Results show that COVID-19 news sentiment influences cryptocurrency returns. The nexus is unidirectional from news sentiment to cryptocurrency returns, in contrast to past findings. These results have practical implications for policymakers and market participants in understanding cryptocurrency market dynamics under extremely stressful market conditions.