Non-linear relationship between oil and cryptocurrencies: Evidence from returns and shocks

The price instabilities between oil prices and cryptocurrencies have motivated the current study to examine the nonlinear relationship between oil returns/shocks and cryptocurrencies during March 3, 2018 to October 10, 2021. We employed a novel methodology of cross-quantilogram to unveil the nonlinearity and asymmetry between oil shocks and cryptocurrencies. We find that when markets are normal and bullish, there is a positive correlation between oil returns and cryptocurrency returns at first lag; however, there is a negative correlation between oil returns and cryptocurrencies in all market conditions. Moreover, rising fluctuations in oil demand shocks brings significant movement in cryptocurrency returns in bearish market conditions and it is unlikely that oil demand shocks and cryptocurrencies returns move in same directions. Given these results, we proposed useful implications for policymakers, strategists, regulators, financial market participants, and investors to hedge/diversify their risk.     

Nonlinear tail dependence in cryptocurrency-stock market returns: The role of Bitcoin futures

We investigate the median and tail dependence between cryptocurrency and stock market returns of BRICS and Developed countries using a newly developed nonparametric cumulative measure of dependence over the period January 4, 2016 – December 31, 2019 as well as before and after the introduction of Bitcoin futures on December 17, 2017. The new measure is model-free and permits measuring tail risk. The results highlight the leading role of S&P500, Nasdaq and DAX 30 in predicting BRICS and developed countries’ stock market returns. Among BRICS countries, BVSP shows a starring role in predicting stock market returns. BSE 30 is the most predictor of cryptocurrencies, which have a little predictability on stock market returns. Ethereum has the leading role in predicting cryptocurrencies and stock market returns followed by Bitcoin. Tail dependence shows substantial role of S&P500, Nasdaq and BVSP in predicting stock market returns. Subsample analysis show the role of Bitcoin futures in reshaping the mean and tail dependence between cryptocurrency and stock market returns. Our results have important policy implications for portfolio managers, hedge funds and investors.     

Good volatility,bad volatility,and the cross section of cryptocurrency returns

This paper examines the predictability of realized volatility measures (RVM), especially the realized signed jumps (RSJ), on future volatility and returns. We confirm the existence of volatility persistence and future volatility is more strongly related to the volatility of past positive returns than to that of negative returns in the cryptocurrency market. RSJ-sorted cryptocurrency portfolios yield statistically and economically significant differences in the subsequent portfolio returns. After controlling for cryptocurrency market characteristics and existing risk factors, the differences remain significant. The investor attention explains the predictability of realized jump risk in future cryptocurrency returns.     

Adverse selection in cryptocurrency markets

In this article we investigate the influence that information asymmetry may have on future volatility, liquidity, market toxicity, and returns within cryptocurrency markets. We use the adverse-selection component of the effective spread as a proxy for overall information asymmetry. Using order and trade data from the Bitfinex exchange, we first document statistically significant adverse-selection costs for major cryptocurrencies. Also, our results suggest that adverse-selection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverse-selection risk on transaction costs in cryptocurrency markets. Finally, we document that adverse-selection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.     

Liquidity in the cryptocurrency market and commonalities across anomalies

We examine how liquidity affects cryptocurrency market efficiency and study commonalities in anomaly performance in cryptocurrency markets. Based on the unique features of cryptocurrencies, we build a model with anonymous traders valuing cryptocurrencies as payments for goods and investment assets, and find that decreases in funding liquidity translate into lower asset liquidity in the cryptocurrency market. Empirically, we observe that many widely recognized stock market anomalies also exist in the cryptocurrency market, although some have opposite long and short legs. We also find evidence that a decrease in cryptocurrency liquidity enhances anomalous returns while preventing the cryptocurrency market from achieving efficiency.     

On spillover effects between cryptocurrency-linked stocks and the cryptocurrency market: Evidence from Australia

This paper categorizes Australian listed cryptocurrency-linked stocks (CLS) by their involvement as a user, developer and diffuser, and investor of blockchain technology and cryptocurrencies based on company announcements and published information on the company websites. By distinguishing CLS engagement with blockchain technology, we examine their returns and volatility spillover with the cryptocurrency market over the period 1 September 2017 to 7 June 2018, spanning important episodes and dynamics in the cryptocurrency market in 2017-2018, and the emergence of Australian CLS. Utilizing the Diebold and Yilmaz (2012) spillover methodology, we find significant unidirectional return spillover and weak volatility spillover from the cryptocurrency market to CLS, after controlling return dynamics of the Australian dollar, Gold and commodity. However, CLS with high involvement in blockchain technology displays stronger connectedness to the cryptocurrency market through return spillover relative to low involvement CLS. Our findings indicate that investors incorporate the price dynamics of cryptocurrencies into their trading decisions for CLS.     

A comparative analysis of cryptocurrency returns and economic policy uncertainty pre- and post-Covid-19

This study analyzes the impact of economic policy uncertainty (EPU) on cryptocurrency returns for a sample of 100 highly capitalized cryptocurrencies from January 2016 to May 2021. The results of the panel data analysis and quantile regression show that increases in global EPU have a positive impact on cryptocurrency returns for lower cryptocurrency returns quantiles and an adverse impact for upper quantiles. In line with the existing literature, the Covid-19 pandemic resulted in higher returns for cryptocurrencies. Inclusion of a Covid-19 dummy in the models strengthened the impact of EPU on cryptocurrency returns. Furthermore, the relationship between the change in EPU and cryptocurrency returns was direct in the pre-Covid-19 period but inverse in the post-Covid-19 period. These results imply that cryptocurrencies act more like traditional financial assets in the post-Covid-19 era.     

MAX momentum in cryptocurrency markets

This paper studies the MAX effect, the relationship between maximum daily returns and future returns in the cryptocurrency market. The cryptocurrency market is an ideal setting for the MAX effect due to its lottery-like features (i.e., large positive skewness). Contrary to findings in other markets, we demonstrate that cryptocurrencies with higher maximum daily returns tend to achieve higher returns in the future and call this the “MAX momentum” effect. We also find that the magnitude of the MAX momentum effect varies with market conditions, investor sentiment and the underpricing of cryptocurrencies. Additionally, this effect is robust to longer holding periods, different MAX measures and alternative sample selection criteria.     

Investor attention in cryptocurrency markets

We examine the relationship between investor attention, and measures of uncertainty, with the market dynamics of Bitcoin and other cryptocurrencies. We find that increases in investor attention are associated with higher returns, more volatility, and greater illiquidity in cryptocurrency markets. In contrast, cryptocurrency uncertainty (UCRY) and financial market uncertainty (VIX) are also positively related to volatility and illiquidity but have a negative contemporaneous relationship with returns. The identified relationships are accentuated during the COVID-pandemic, and are robust to different measures of investor attention, volatility, and illiquidity. Our results suggest that monitoring investor attention could assist both investors and policymakers.     

Asymmetric interdependencies between large capital cryptocurrency and Gold returns during the COVID-19 pandemic crisis

This article explores asymmetric interdependencies between the twelve largest cryptocurrency and Gold returns, over the period January 2015 – June 2020 within a NARDL (nonlinear autoregressive distributed lag) framework. We focus our analysis on the epicentre of the first wave of the COVID-19 pandemic from March 2020 to June 2020. During this crisis, cryptocurrencies are more correlated and more of them have returns that are cointegrated with Gold returns. Moreover, cryptocurrencies develop a long-term as well as a short-term asymmetric response to Gold returns during the COVID-19 period where most cryptocurrency returns respond more to negative changes and exhibit more persistence with Gold returns. Overall, our most important result confirms that the connectedness between Gold price returns and cryptocurrency returns increase in economic turmoil, such as during the COVID-19 crisis.     

Cryptocurrencies: Stylized facts on a new investible instrument

We present stylized facts on the asset pricing properties of cryptocurrencies: summary statistics on cryptocurrency return properties and measures of common variation for secondary market returns on 222 digital coins. In our sample, secondary market returns of all other currencies are strongly correlated with Bitcoin returns. We also provide some investment characteristics of a sample of 64 initial coin offerings.     

Are cryptocurrencies homogeneous?

This article investigates if cryptocurrencies returns' are similarly affected by a selection of demand- and supply-side determinants. Homogeneity among cryptocurrencies is tested via a least absolute shrinkage and selection operator (LASSO) model where determinants of Bitcoin returns are applied to a sample of 12 cryptocurrencies. The analysis goes beyond existing research by simultaneously covering different periods and design choices of cryptocurrencies. The results show that cryptocurrencies are heterogeneous, apart from some similarities in the impact of technical determinants and cybercrime. The cryptocurrency market displays evidence of substitution effects, and design choices related explain the impact of the determinants of return.     

The entry and exit dynamics of the cryptocurrency market

This paper presents an analysis of the entry and exit dynamics of the cryptocurrency market that focuses on the growth of initial coin offerings during 2015–2020. We used two different datasets: one includes long-lived cryptocurrencies, while the other includes the whole cryptocurrency system at our disposal–that is, it considers the entering and exiting cryptocurrencies. Comparing the dynamics between both datasets with the index cohesive force approach, we assessed how the growth of the initial coin offerings and the exiting cryptocurrencies affected the connectedness of the market. Our results show that the expansion of the cryptocurrency system gave rise to a strong collective movement during 2018–2019. Afterwards, the group pressure, due to the bubble of the initial coin offerings, decreased in favour of the largest cryptocurrencies. Lastly, we observed changes in the hierarchical order of the most influential cryptocurrencies. In particular, Ethereum became the most influential cryptocurrency, at the detriment of Bitcoin.     

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.     

Conditional tail-risk in cryptocurrency markets

In this paper we use $CoVaR$ to estimate the conditional tail-risk in the markets for bitcoin, ether, ripple and litecoin and find that these cryptocurrencies are highly exposed to tail-risk within cryptomarkets, while they are not exposed to tail-risk with respect to other global assets, like the U.S. equity market or gold. Although cryptocurrency returns are highly correlated one with the other, we find that idiosyncratic risk can be significantly reduced and that portfolios of cryptocurrencies offer better risk-adjusted and conditional returns than individual cryptocurrencies. These results indicate that portfolios of cryptocurrencies could offer attractive returns and hedging properties when included in investors’ portfolios. However, when we account for liquidity, the share of crypto assets in investors’ optimal portfolio is small.     

High frequency momentum trading with cryptocurrencies

Over the past few years, cryptocurrencies have increasingly been discussed as alternatives to traditional fiat currencies. These digital currencies have garnered significant interest from investment banks and portfolio managers as a potential option to diversify the financial risk from investing in other assets. This interest has also extended to the general public who have seen cryptocurrencies as a way of making a quick profit. This paper provides a first insight into the applicability of high frequency momentum trading strategies for cryptocurrencies. We implemented two variations of a signal-based momentum trading strategy: (i) a time series method; (ii) a cross sectional method. These strategies were tested on a selection of seven of the largest cryptocurrencies ranked by market capitalization. The results show that there exists potential for the momentum strategy to be used successfully for cryptocurrency trading in a high frequency setting. A comparison with a passive portfolio strategy is proposed, which shows abnormal returns when compared with the momentum strategies. Furthermore, the robustness of our results are checked through the application of the momentum strategies other sample periods. We also compare the performances of the signal-based momentum strategies with returns-based versions of the strategies. It is shown that the signal-based strategy outperforms the returns-based strategy. However, there appears to be no single parameterization of the signal-based strategies that can generate the greatest cumulative return over all sample periods.     

Can salience theory explain investor behaviour? Real-world evidence from the cryptocurrency market

Research on human attention indicates that objects that stand out from their surroundings, i.e., salient objects, attract the attention of our sensory channels and receive undue weighting in the decision-making process. In the financial realm, salience theory predicts that individuals will find assets with salient upsides (downsides) appealing (unappealing). We investigate whether this theory can explain investor behaviour in the cryptocurrency market. Consistent with the theory's predictions, using a sample of 1738 cryptocurrencies, we find that cryptocurrencies that are more (less) attractive to “salient thinkers” earn lower (higher) future returns, which indicates that they tend to be overpriced (underpriced). On average, a one cross-sectional standard-deviation increase in the salience theory value of a cryptocurrency reduces its next-week return by 0.41%. However, the salience effect is confined to the micro-cap segment of the market, and its size is moderated by limits to arbitrage.     

Return and volatility properties: Stylized facts from the universe of cryptocurrencies and NFTs

Stylized facts of returns and volatility are an important approximation tool for empirical finance studies, especially in the area of young and new assets. In this paper, we examine the return and volatility properties of four non-fungible tokens (NFTs) and four cryptocurrencies from 24th January 2018–2nd August 2022. The results show the following: Firstly, the returns of both NFTs and cryptocurrencies have fat tails, with evidence of tail exponents following the inverse cubic-law, along with clear persistence behavior. Secondly, all returns exhibit volatility clustering, albeit to varying degrees, and the detected absence of inverse volatility-asymmetry challenges the safe-haven property often documented for cryptocurrencies. Thirdly, return-based long-memory is slightly more intense than volatility-based long-memory, especially for NFTs, which demonstrate a predictability contesting market efficiency. These findings are generally consistent with previous findings on equities, implying that the return and volatility behavior of NFTs and cryptocurrencies is leaning towards that of traditional assets.     

Effect of twitter investor engagement on cryptocurrencies during the COVID-19 pandemic

This study aims to examine whether the prices and returns of two cryptocurrencies, Dogecoin and Ethereum, are affected by Twitter engagement following the COVID-19 pandemic. We use the autoregressive integrated moving average with explanatory variables model to integrate the effects of investor attention and engagement on Dogecoin and Ethereum returns using data from December 31, 2020, to May 12, 2021. The results provide evidence supporting the hypothesis of a strong effect of Twitter investor engagement on Dogecoin returns; however, no potential impact is identified for Ethereum. These findings add to the growing evidence regarding the effect of social media on the cryptocurrency market and have useful implications for investors and corporate investment managers concerning investment decisions and trading strategies.     

Dependence and risk spillovers among clean cryptocurrencies prices and media environmental attention

This paper examines the relationships among cryptocurrency environmental attention and clean cryptocurrencies prices using Time-Varying Parameter Vector Auto-Regression (TVP-VAR) and wavelets techniques. Results show strong connectedness among these variables, implying that the prices of clean cryptocurrencies are influenced by attention on cryptocurrency sustainability. Connectedness is stronger with positive shocks on environmental attention than negative shocks. Also, in the short-term, clean cryptocurrencies prices lead environmental attention, especially after 2021. However, there are notable periods when environmental attention led clean cryptocurrency prices before 2021. In the long-term, clean cryptocurrencies such as Hedera, Polygon, Cosmos, IOTA, TRON, Stellar, Tezos and Ripple lead environmental attention. In the presence of bitcoin, the degrees of connectedness increased across both shocks on cryptocurrency environmental attention. In all cases, the bitcoin market is the main destination of shocks from the system. We highlight some crucial implications of these results.