Cryptocurrency Returns Research Articles

A bibliometric review of cryptocurrencies as a financial asset

ABSTRACT Within a decade, cryptocurrencies have captured significant attention. After Bitcoin's emergence in 2008, new cryptocurrencies started to enter the financial market. We use bibliometric analysis to explore the cryptocurrency literature in the areas of business and management. We review and analyze 464 research articles through bibliometric measurements and social network analysis using Biblioshiny in R. Our study highlights the most influential authors, institutions, countries, and studies. Using the results of the social network analysis of the authors and countries, we illustrate the co-authorship and collaboration among authors from various countries at the institutional level and demonstrate how they have expanded the knowledge in this area. We identify four streams in the current cryptocurrency literature: (i) the determinants of cryptocurrency returns, (ii) the efficiency of cryptocurrencies, (iii) tests of portfolio diversification and sheep flock behaviour and (iv) the regulation, governance, and socio-economic impact of cryptocurrencies. Finally, we present an agenda for future research in the field.

Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk

We study portfolio optimization of four major cryptocurrencies. Our time series model is a generalized autoregressive conditional heteroscedasticity (GARCH) model with multivariate normal tempered stable (MNTS) distributed residuals used to capture the non-Gaussian cryptocurrency return dynamics. Based on the time series model, we optimize the portfolio in terms of Foster-Hart risk. Those sophisticated techniques are not yet documented in the context of cryptocurrency. Statistical tests suggest that the MNTS distributed GARCH model fits better with cryptocurrency returns than the competing GARCH-type models. We find that Foster-Hart optimization yields a more profitable portfolio with better risk-return balance than the prevailing approach.

Are Cryptos Safe-Haven Assets during Covid-19? Evidence from Wavelet Coherence Analysis

ABSTRACT This study adds to the inconclusive debate on safe-haven properties of cryptocurrencies during Covid-19 by analyzing the use of wavelet coherence framework on the global Covid-19 fear index, cryptocurrency implied volatility index (VCRIX), and cryptocurrency returns. Our findings show that a non-financial market-based proxy of market stress that represents fear of households and retail investors reveals cryptocurrencies as safe-haven assets; however, a financial market-based proxy of market turbulence exposes that cryptocurrencies behave like traditional assets during the times of Covid-19 pandemic. Our findings support that long-term investors can invest in the cryptocurrency market to hedge the risks during Covid-19 pandemic.

On the (in)efficiency of cryptocurrencies: have they taken daily or weekly random walks?

The legitimacy of virtual currencies as an alternative form of monetary exchange has been the centre of an ongoing heated debated since the catastrophic global financial meltdown of 2007–2008. Our study tests the informational market efficiency of cryptomarkets by investigating the weak-form efficiency of the top-five cryptocurrencies using random walk testing procedures which are robust to asymmetries and unobserved smooth structural breaks. Moreover, our study employs two frequencies of cryptocurrency returns, one corresponding to daily returns and the other to weekly returns. Our findings validate the random walk hypothesis for daily series hence validating the weak-form efficiency for daily returns. On the other hand, weekly returns are observed to be stationary processes which is evidence against weak-form efficiency for weekly returns. Overall, our study has important implications for market participants within cryptocurrency markets.

Diversification benefits in the cryptocurrency market under mild explosivity

We investigate whether cryptocurrencies provide diversification benefits to risk averters via a stochastic spanning methodology. We avoid the conceptual and statistical problems of non-stationary returns by providing a modification of the second order stochastic dominance relation and of the related notion of stochastic spanning. These are compatible with a mildly explosive framework for the logarithm prices, along with conditions for asymptotic negligibility of bubbles. In the empirical application, we construct optimal portfolios, both with and without cryptocurrencies, and evaluate their comparative performance both in- and out-of-sample. A conservative modification of a t-test is presented to test the null hypothesis of non-dominance of an optimal portfolio that includes cryptocurrencies over the traditional portfolio of only stocks, bonds and cash. The augmented portfolio is found to be a good diversification option for some risk averse investors in the full sample period and in a sub-period of high cryptocurrency returns.

Predictive role of online investor sentiment for cryptocurrency market: Evidence from happiness and fears

We examine the predictive ability of online investor sentiment for six major cryptocurrency returns. For this, we use two proxies, the FEARS index of Da et al. (2015) and Twitter Happiness sentiment, applying the bivariate cross-quantilogram of Han et al. (2016). Happiness sentiment index significantly predicts Bitcoin return as well as other major cryptocurrencies at the two extreme states of the market and for extreme levels of sentiment. Hence, investors should readjust their portfolios according to the market sentiment and limit their decision on the safe-haven property of Bitcoin. As to FEARS, predictability also exists but is rather pronounced for a low level of sentiment. Overall, Happiness sentiment reveals to be a persistent and robust predictor for most cryptocurrency returns. FEARS index also shows significant predictability of returns, but the predictability is weaker and mainly in the short-term. In summary, our findings provide evidence that online investor sentiment is a significant nonlinear predictor for most major cryptocurrencies returns, suggesting though the superiority of Twitter to Google-based online investor sentiment proxy. Moreover, cryptocurrency returns seem to be driven more by sentiment transmitted through social media than with macroeconomic news, which is in line with the nature of cryptocurrency participants, mainly young individuals computer enthusiasts.

Investor attention and cryptocurrency: Evidence from wavelet-based quantile Granger causality analysis

This paper investigates the relationship between investor attention and the major cryptocurrency markets by wavelet-based quantile Granger causality. The wavelet analysis illustrates the interdependence between investor attention and the cryptocurrency returns. Multi-scale quantile Granger causality based on wavelet decomposition further demonstrates bidirectional Granger causality between investor attention and the returns of Bitcoin, Ethereum, Ripple and Litecoin for all quantiles, except for the medium. Among them, the Granger causality from investor attention to the returns is relatively very weak for Ethereum. In the short term, the Granger causality from these cryptocurrency returns to investor attention seems symmetric, but in the medium- and long- term, the causality shows some asymmetry. The Granger causality from investor attention to these cryptocurrency returns is asymmetric and varies across cryptocurrencies and time scales. Specifically, investor attention has a relatively stronger impact on the cryptocurrency returns in bearish markets than that in bullish markets in the short term.

Liquidity risk and expected cryptocurrency returns

AbstractThis paper examines how liquidity risk is priced in the cross‐section of cryptocurrency returns. In doing so, we use the Amihud measure as a liquidity proxy. By employing the univariate portfolio analysis, the bivariate portfolio analysis, and the Fama‐MacBeth regression analysis, we document a negative relationship between liquidity and cryptocurrency returns. Additional tests demonstrate that this finding is robust to alternative liquidity measurement as well as size screens and show no evidence of a significant intertemporal relationship between liquidity and expected returns for three leading cryptocurrencies. Our conclusions add to the understanding of how markets price cryptocurrencies.

Nonlinear nexus between cryptocurrency returns and COVID–19 news sentiment

This paper examines how different categories of COVID–19 news sentiment differentially impact the behavior of cryptocurrency returns. A nonlinear technique of transfer entropy is applied to investigate the relationship between the top 30 cryptocurrencies by market capitalization and COVID–19 news sentiment to capture cryptocurrency return and volatility dynamics. 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 stressful market conditions.

Risk Analysis of Cryptocurrency

We compare the explainability of cryptocurrency returns from macro and microeconomic risk factors during stressed and normal market environments, in particular, analyzing the effects of the COVID-19 pandemic to cryptocurrency return explainability. We find that risk-premiums are encapsulated within cryptocurrency-specific market factors in both stressed and normal market conditions. Furthermore, cryptocurrency factors, particularly relating to liquidity, momentum, and counterparty risk, showed evidence of providing stronger predictability of cryptocurrency returns during the COVID-19 pandemic compared to pre-pandemic levels. We find that during the stressed market environment, Fama-French 5 factors continue to provide low explainability to cryptocurrency returns.

Good Volatility, Bad Volatility, and the Cross Section of Cryptocurrency Returns

This paper examines the distributional properties of cryptocurrency realized variation measures (RVM) and the predictability of RVM on future returns. We show the cryptocurrency volatility persistence and the importance of the asymmetry on volatility forecasting. Signed jumps variations contribute around 18% of the cryptocurrency return quadratic variations. The realized signed jump (RSJ) strongly predicts the cross-sectional future excess returns. Sorting the cryptocurrencies into portfolios sorted by RSJ yields statistically and economically significant differences in future excess returns. This jump risk premium remains significant after controlling for cryptocurrency market characteristics and existing risk factors. The standard cross-sectional regression convinces the cryptocurrency return predictability from RSJ by controlling multiple cryptocurrency characteristics. The investor attention explains the predictability of realized jump risk in future cryptocurrency returns.

GPU Prices and Cryptocurrency Returns

We look at the association between the price of a cryptocurrency and the secondary market prices of the hardware used to mine it. We find the prices of the most efficient Graphical Processing Units (GPUs) for Ethereum mining are significantly positively correlated with the daily price returns to that cryptocurrency.

Cryptocurrency Returns and Cryptocurrency Uncertainty: A Time-Frequency Analysis

We investigate how uncertainty surrounding cryptocurrencies impacts returns in cryptocurrencies using wavelet techniques. Relying on a newly released UCRY index representing cryptocurrency uncertainty, we find UCRY to significantly predict cryptocurrency returns (CR) in all frequencies. Moreover, the UCRY-CR nexus is not influenced by other global uncertainty indexes. The significant co-movement of UCRY-CR remains unchanged for short-, medium- and long-term investment horizons when the effects of economic policy uncertainty and volatility index are removed. Thus, the UCRY-CR relationship is persistent and pervasive.

Accounting for Cryptocurrency Value

Information pertaining to new addresses is highly value-relevant in the cryptocurrency market. Innovations to the number of new addresses explain eight percent of the variations in cryptocurrency returns. Unlike traditional markets, we do not find evidence of pre- or post-drift around the release of new address information. The presence of strong market reactions at the release of new address information and the absence of drifts around the release highlight the distinct features of cryptocurrencies and provide a benchmark case where information is released publicly and continuously. Lastly, we show that the price-to-new address ratios negatively predict future cryptocurrency returns.

How Common are Return Factors in Cryptocurrencies and Equities?

Finance research contributes to finding factors to explain the cross-sectional expected equity and cryptocurrency returns. In this paper, we implement the statistical methods to evaluate the common factor structure of these two markets and aim to identify the possible common factors across two markets. By considering the comprehensive dataset covering cryptocurrencies and the U.S. equity universe, we find that there are no common pervasive factors that govern the returns for both cryptocurrencies and equities before 2019, but there arises one common factor post-2020. The identified common factor is significantly related to the standard equity factors but not the crypto factors, which suggests that equity factors can help explain the common variations in returns across equities and cryptocurrencies. The documented pattern is robust across different U.S. equity market classifications, return specifications, and implemented statistical methods. Moreover, we find this pattern holds at the international equity market level. Our results point to the arising of common factors driving the returns on these two markets. Specifically, it's the equity factors that contribute to explaining the cross-sectional cryptocurrency returns recently.

Price Stability of Cryptocurrencies as a Medium of Exchange

We present positive evidence of price stability of cryptocurrencies as a medium of exchange. For the sample years from 2016 to 2020, the prices of major cryptocurrencies are found to be stable, relative to major financial assets. Specifically, after filtering out the less-than-one-month cycles, we investigate the daily returns in US dollars of the major cryptocurrencies (i.e., Bitcoin, Ethereum, and Ripple) as well as their comparators (i.e., major legal tenders, the Euro and Japanese yen, and the major stock indexes, S&P 500 and MSCI World Index). We examine the stability of the filtered daily returns using three different measures. First, the Pearson correlations increased in later years in our sample. Second, based on the dynamic time-warping method that allows lags and leads in relations, the similarities in the daily returns of cryptocurrencies with their comparators have been present even since 2016. Third, we check whether the cumulative sum of errors to predict cryptocurrency prices, assuming stable relations with comparators' daily returns, does not exceeds the bounds implied by the Black-Scholes model. This test, in other words, does not reject the efficient market hypothesis.

Cryptocurrencies Meet Equities: Risk Factors and Asset Pricing Relationships

We consider a variety of cryptocurrency and equity risk factors as potential forces that drive cryptocurrency returns and carry risk premiums. In a cross-section of 2,000 biggest cryptocurrencies, only downside market risk, cryptocurrency size and policy uncertainty factors are systematically priced with significant premiums. Momentum premium vanished in the recent years. Equity market risk appears to be more important than cryptocurrency market risk, suggesting greater linkages between cryptocurrency and equity markets than we used to think.

Cryptocurrency Factor Portfolios: Performance, Decomposition and Pricing Models

The empirical distributions of cryptocurrency returns are highly non-normal, casting doubt on the performance metrics. So we apply almost stochastic dominance (ASD), which does not require any assumption about the return distribution, to examine cryptocurrency factor portfolios. Using portfolios based on factors that can be constructed from available market information, we find 13 factor portfolios that dominate our four benchmarks. The long-only strategy contributes more to this dominance than does the short-only strategy. We test whether returns on the 13 dominant factor portfolios can be explained by a coin market three-factor model. This model has limited success, and its performance is significantly improved by the inclusion of a mispricing factor.

A Factor Model for Cryptocurrency Returns

A Factor Model for Cryptocurrency Returns

A Factor Model for Cryptocurrency Returns

We investigate the dynamics of daily realised returns and risk premiums for a large cross-section of cryptocurrency pairs through the lens of an Instrumented Principal Component Analysis (IPCA) (see Kelly et al. 2019). We show that a model with three latent factors and time-varying factor loadings significantly outperforms a benchmark model with observable risk factors: the total (predictive) R^2 from the IPCA is 17.2% (2.9%) for individual returns, against a benchmark 9.6% (-0.02%) obtained from a model with six observable risk factors explored in previous literature. By looking at the characteristics that significantly matter for the dynamics of risk premiums, we provide robust evidence that liquidity, size, reversal, and both market and downside risks represent the main driving factors behind expected returns. These results hold for both individual assets and characteristic-based portfolios, pre and post the Covid-19 outbreak, and for weekly individual and portfolio returns.