Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors

Cryptocurrencies are gradually establishing themselves as a new class of assets with unique features, although there remains skepticism and a lack of understanding of their nature. In this study, we compare the financial properties of these new digital assets and investigate their dynamic relationship with major financial securities and commodities. Furthermore, we evaluate the economic and financial potential benefits of cryptocurrencies for financial investors. Using different time-varying copula approaches and bivariate dynamic conditional correlation GARCH models, we find that the cross-correlation with conventional assets is changing over time but weak, supporting the idea that these cryptocurrencies can be suitable for financial diversification. However, our analysis of portfolios shows that cryptocurrencies are poor hedging instruments in most of the considered cases. Moreover, we find that the relationship between cryptocurrencies and conventional assets is sensitive to external economic and financial shocks.     

Time and frequency connectedness of green equity indices: Uncovering a socially important link to Bitcoin

Green investment funds continue to interest as a sustainable non-conventional asset class. We examine their interconnectedness, using network and wavelet analyses, with both traditional and non-traditional financial assets. Results indicate that global stock market performance, along with the returns of emerging markets, commodity markets, and FinTech are strongly correlated with green indices. However, in comparison, Bitcoin is found to be isolated, as confirmed by wavelet analyses. When considering the evolution of green investment indices, their role as diversifiers to Bitcoin is especially interesting, suggesting many potential benefits for investors and policymakers. Perhaps the most prominent application of our results is for Bitcoin investors to consider more closely investing in green funds as an offset to concerns about the negative environmental consequences of investing in proof-of-work cryptocurrencies.     

Dynamic volatility transmission and portfolio management across major cryptocurrencies: Evidence from hourly data

This study used hourly data to examine the dynamic conditional correlations and hedging strategies in the main cryptocurrency markets: Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), and Ripple (XRP). Multivariate generalized autoregressive conditional heteroskedasticity family models provided evidence of significant positive dynamic conditional correlations among these markets. A weaker conditional correlation was observed for the LCT–XRP portfolio than for the BTC–ETH portfolio, which had the highest correlation value. The dynamic correlations intensified after the cryptocurrency crisis. The results of a portfolio risk analysis suggested that investors should hold less BTC than LTC, ETH, and XRP to minimize risk while maintaining consistent expected portfolio returns. Investors should hold less BTC than the other cryptocurrencies during a crisis. In addition, the cheapest hedge strategy is to hold long BTC and short XRP regardless of the period. Holding long BTC and short LTC was found to be the most expensive hedge strategy. Finally, the study showed that an optimally weighted diversified portfolio provides the greatest reduction in risk and downside risk for ETH and LTC. For XRP, portfolio hedging is the best mechanism for reducing risk.     

Cryptocurrencies and asset pricing

We demonstrate that investors obtain abnormal returns by trading cryptocurrencies daily on the London Stock Exchange from 2014–2017. Excess returns persist once we account for systematic risk, size, value, momentum, profitability and investment. Investor abnormal returns in cryptocurrencies implies inefficiency.     

Volatility cascades in cryptocurrency trading

This paper studies volatility cascades across multiple trading horizons in cryptocurrency markets. Using one-minute data on Bitcoin, Ethereum and Ripple against the US dollar, we implement the wavelet Hidden Markov Tree model. This model allows us to estimate the transition probability of high or low volatility at one time scale (horizon) propagating to high or low volatility at the next time scale. We find that when moving from long to short horizons, volatility cascades tend to be symmetric: low volatility at long horizons is likely to be followed by low volatility at short horizons, and high volatility is likely to be followed by high volatility. In contrast, when moving from short to long horizons, volatility cascades are strongly asymmetric: high volatility at short horizons is now likely to be followed by low volatility at long horizons. These results are robust across time periods and cryptocurrencies.     

Governance and control in distributed ledgers: Understanding the challenges facing blockchain technology in financial services

As blockchain platforms are becoming increasingly noticeable in financial services and beyond, questions arise regarding their suitability to compete with or replace existing payment systems and marketplaces and redesign the financial infrastructures of the future. Prominent among these concerns are issues around governance and control in distributed ledgers: How are distributed ledger technologies governed? Can blockchains address complex administration problems? What key issues of note for practitioners and academics have emerged thus far? In this paper we aim to review the existing governance practices of established or popular blockchain and decentralized autonomous organization (DAO) systems with a view to understanding how they hold up in times of crises. What questions are raised when they are compromised or faced consensus challenges in coordinating action especially around control and accountability? We use a translational process, generating focal insights about present concerns from the reference point of completed academic studies and extensive practitioner consultation. Rather than adopting a declarative approach attempting to provide all the answers, we draw insights from the IT platform governance literature to offer a critical perspective for asking the right questions around key governance issues in financial infrastructure such as decision rights, control mechanisms, and incentives.     

The double-edged impact of social media on online trading: Opportunities,threats, and recommendations for organizations

While we understand well how social media channels sway consumers, there is little understanding of their influence on online trading behavior. We argue that social media are creating a new class of self-directed online traders by simultaneously encouraging and biasing trading decisions. Through an empirical study, we show that heavy social media users are more likely to engage in online trading but are largely affected by online herding behavior, and are four times more likely to blindly follow other traders. Bloggers, influencers, social network contacts, and social media news shape these users’ online trading behaviors. As online traders influenced by social media are unlikely to receive adequate returns, companies face an ethical dilemma: They could leverage social media to efficiently access funds but they risk inappropriately exploiting the inexperience of online traders biased by social media. We offer a set of nine practical recommendations for organizations to respond to these new challenges.     

Value at risk and returns of cryptocurrencies before and after the crash: long-run relations and fractional cointegration

Cryptocurrency has become an increasingly important investment vehicle, thus the long-run relationship between risk and return of cryptocurrency is vital for both investors and policy-makers. We apply the Fractionally Cointegrated Vector Autoregression (FCVAR) model and investigate the risk-return relationship. This has not been studied previously, much less disintegrating the series into periods of pre-crash, post-crash, and the full sample. Empirical results indicate that risk series in all eight cryptocurrency markets exhibit long-memory property, and there is a long-run fractional cointegration relationship between the risk of altcoins and Bitcoin. Most importantly, though a positive risk-return tradeoff is found in the full sample, there are big differences between results of pre-crash and post-crash. The time horizon set to 14 days, all the eight currencies exhibit traditional risk-return tradeoff after the crisis, whereas the effect doesn’t exist before the crisis, with the exception of Dash and Doge. In the same vein, there is no leverage effect in all eight currencies before the crisis but this effect is present in three cryptocurrencies after the crisis. The differences indicate that investors are more cautious, increasing risk awareness and demanding higher compensation for risk after the crash. The above results are robust when time horizon is 30 days.     

A systematic review of the bubble dynamics of cryptocurrency prices

This paper surveys the academic literature concerning the formation of pricing bubbles in digital currency markets. Studies indicate that several bubble phases have taken place in Bitcoin prices, mostly during the years 2013 and 2017. Other digital currencies of primary importance, such as Ethereum and Litecoin, also exhibit several bubble phases. The Augmented Dickey Fuller (ADF) as well as the Log-Periodic Power Law (LPPL) methodology are the most frequently employed techniques for bubble detection and measurement. Based on much academic research, Bitcoin appears to have been in a bubble-phase since June 2015, while Ethereum, NEM, Stellar, Ripple, Litecoin and Dash have been denoted as possessing bubble-like characteristics since September 2015. However, this latter group possess little academic evidence supporting the presence of bubbles since early 2018. An overall perspective is provided based on a robust bibliography based on large deviations of market quotes from fundamental values that can serve as a guide to policymakers, academics and investors.     

Lottery-like momentum in the cryptocurrency market

Following the methodology of Bali et al. (2011), we construct the lottery-like portfolio based on the maximum return. First, we find that a higher maximum return leads to a higher future return among 64 cryptocurrencies. This phenomenon is called the lottery-like momentum. Controlling for the momentum effect, the lottery-like momentum still exists in the cryptocurrency market. In addition, we find that the major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC)—are less likely to have extreme positive returns. And the absence of extreme positive returns is persistent.