Narrow Results

We study the tails’ behavior of four major Cryptocurrencies (Bitcoin, Litecoin, Ethereum and Ripple) by employing the Autoregressive Fr´echet model for conditional maxima. Using five-minute-high-frequency data, we report time-evolving tails as well as provide a straightforward measure of tails asymmetry for positive and negative intra-day returns. We find that only Bitcoin has a notable more massive tail for positive returns asymmetry while the remaining three Cryptocurrencies have a general tendency towards more massive negative intra-day tails. All considered Cryptocurrencies depict lighter tails as the market matures.

This study explores the connectedness between cryptocurrencies (Bitcoin, Ethereum, Ripple, Bitcoin cash and Ethereum Operating System) and major stock markets (NYSE composite index, NASDAQ composite index, Shanghai Stock Exchange, Nikkei 225 and Euronext NV). Using the asymmetric dynamic conditional correlation (ADCC) and wavelet coherence approaches, we document a significant time-varying conditional correlation between the majority of the cryptocurrencies and stock market indices and that the negative shocks play a more prominent role than the positive shocks of the same magnitude. Overall, our findings explore potential avenues for diversification for investors across cryptocurrencies and major stock markets.

This study explores the return and volatility spillovers between S&P 500 and cryptocurrencies [Litecoin (LTC), Bitcoin (BTC) and Ethereum (ETH)] during the pre-COVID-19 period and COVID-19 period using the VAR–BEKK–AGARCH model on hourly data. Furthermore, this study also quantifies the optimal portfolio weights and hedge ratios during both sample periods. The findings of study show that the return and volatility spillovers between the US stock and cryptocurrency markets are not significant during the pre-COVID-19 period. However, the study finds unidirectional return transmission from S&P 500 to all the cryptocurrencies during the COVID-19 period. During the COVID-19 period, the volatility spillover is unidirectional from S&P 500 to Litecoin, whereas the volatility transmissions are not significant for the pairs of S&P 500–Bitcoin and S&P 500–Ethereum. Based on optimal weights, the portfolio managers are recommended to slightly decrease their investments in S&P 500 for the portfolios of S&P 500/BTC, S&P 500/ETH and S&P 500/LTC during the COVID-19 period. Finally, during the COVID-19 period, all hedge ratios were found to be higher, implying higher hedging costs during the COVID-19 period compared to the pre-COVID-19 period. Our research offers valuable insights to the fund managers, investors and policymakers regarding diversification opportunities, hedging, optimal asset allocation and risk management.

Cryptocurrencies have emerged as a new asset class. In order to provide a thorough understanding of this new asset class, we study the dependencies in tail risk events within cryptocurrencies, and provide a hedging alternative in this paper. First, we adopt the Financial Risk Meter approach for cryptocurrencies, which is able to identify individual risk characteristics and indicate systemic risk in a network topology. Next, we detect the interdependencies across digital coins and study the spillover effects. Finally, we construct tail event sensitive portfolios and test the performance versus traditional approaches from January 2019 to May 2022.

In view of explosive trends and excessive trades in the cryptocurrency markets, this paper contributes to the existing literature by bringing in the limelight the effect of liquidity on the herding behavior in the cryptocurrency market. Results from a first applied herding model including contemporaneous and lagged squared market returns demonstrated that market-wide herding exists within falling markets. The incorporation of liquidity highlights further evidences on herding behavior across cryptocurrencies during high and low liquid days, which varies across percentiles. Our findings bring handy implications for topics of portfolio and risk management, as well as regulation.

Cryptocurrencies play a pivotal role in the financial market. Given this, we perform the asymmetric multifractal cross-correlation analysis to examine the weak form of the Efficient Market Hypotheses (EMH) considering two temporal scales. In the daily scale, we find that the pair Bitcoin–Litecoin displays the largest multifractal spectrum. While, in the hourly scale, the pair Bitcoin–Ethereum presents the largest multifractal spectrum. Our empirical evidence has rejected the weak form of the EMH and clearly suggests that the dynamics of the analyzed cryptocurrency pairs are in line with the Fractal Market Hypothesis (FMH). Cross-correlation asymmetries are more persistent for small fluctuations than for large fluctuations. The results are essential for investors, portfolio and risk managers, and policymakers.

This paper performs the asymmetric multifractal cross-correlation analysis to examine the COVID-19 effects on three relevant high-frequency fiat currencies, namely euro (EUR), yen (YEN) and the Great Britain pound (GBP), and two cryptocurrencies with the highest market capitalization and traded volume (Bitcoin and Ethereum) considering two periods (Pre-COVID-19 and during COVID-19). For both periods, we find that all pairs of these financial assets are characterized by overall persistent cross-correlation behavior $({\alpha }_{xy}(0)>0.5)$. Moreover, COVID-19 promoted an increase in the multifractal spectrum’s width, which implies an increase in the complexity for all pairs considered here. We also studied the Generalized Cross-correlation Exponent, which allows us to verify that there is no asymmetric behavior between Bitcoin and fiat currencies and between Ethereum and fiat currencies. We conclude that investing simultaneously in major fiat currencies and leading cryptocurrencies can reduce the portfolio risk, leading to improvement in the investment results.

We take the novel Twitter-based economic uncertainty (TEU) to examine if it has cross-correlation characteristics with four major cryptocurrencies i.e. Bitcoin, Ethereum, Litecoin, and Ripple. To conduct a more thorough analysis, we apply multifractal detrended cross-correlation analysis (MFDCCA) on seasonal-trend decomposition using Loess (STL) decomposed series as well as without decomposed series on the daily data, ranging from 1 June 2011 to 30 June 2021. The findings of this study indicate that: (i) all pairs of TEU with cryptocurrencies are multifractal and have power-law behavior; (ii) the pairs of Ethereum and Bitcoin with TEU are found to be the most multifractal while Litecoin with TEU has the lowest multifractal characteristics; (iii) all STL decomposed series of cryptocurrency have persistent cross-correlation with TEU with the exception of Ethereum which has anti-persistent cross-correlation with TEU; (iv) all without decomposed series of cryptocurrencies show significant persistent cross-correlation characteristics with TEU; (v) the highest linkage is found for the pair of Bitcoin with TEU. Moreover, to reveal the dynamic characteristics in the cross-correlation of TEU with cryptocurrencies, the rolling window is employed for MFDCCA. These findings have important managerial and academic implications for policymakers, investors, and market participants.

The literature has recently begun to investigate the properties of cryptocurrency markets to identify key drivers for the use of cryptocurrencies in investment strategies. This paper provides a comprehensive review on the financial applications of Bitcoin. The focus is on three lines of research: price formation, detection of market inefficiency, and diversified portfolio construction. Topics such as market micro-structure and the interplay between different cryptocurrencies are only touched on briefly. We observe that many empirical studies find that Bitcoin markets are inefficient, with huge price fluctuations and long-range memory, and that these markets are heavily influenced by news and sector-specific events, or by infrastructure conditions such as volume trading and market liquidity. Nevertheless, astonishing price appreciations and modest correlation values versus other asset classes have contributed significantly to motivate applications of Bitcoin to investment and diversification. Future research may address practical implementations of such solutions and investigate the long-term sustainability and viability of these investment strategies.

As Cryptocurrencies are emerging as a new class of investment assets, understanding their price and volatility dynamics has begun to gather momentum, especially the volatility can influence investment decisions. Most of previous literature concentrates primarily on several aspects of Bitcoin and endeavoring to generalize them for the whole cryptocurrency markets. In this study, we attempted to examine the return and volatility spillover effects across a wide range of cryptocurrency markets, i.e. eight major cryptocurrencies (determined by market capitalization) using a Vector Error Correction approach and Diagonal BEKK Multivariate GARCH model. We found the evidence of interdependencies and volatility co-movements among the various pairs of cryptocurrency markets. However, the study suggests that there exists a limited window of opportunity for the short-term portfolio diversification benefits from the selected large-cap cryptocurrency markets.

This paper empirically examines the convergence of cryptocurrency markets with particular attention to top 30 cryptocurrencies. The study applies the novel Phillips and Sul panel convergence technique to daily closing price data of 30 cryptocurrencies for the period October 4, 2017 to May 31, 2020. The empirical findings suggest the evidence of divergence and the existence of club convergence across the cryptocurrency markets. The study finds the existence of five clubs in the top 30 cryptocurrency markets. The outcome of the study helps the investors and crypto lovers to diversify their portfolio by seeing the common transition path of the group of currencies.

In this paper, we measure the risk interdependence of 12 major cryptocurrencies before and during the COVID-19 pandemic, based on a GARCH-Copula-VaR approach and a dynamic network analysis. We find that cryptocurrencies generally show high levels of volatility, speculation, homogeneity and tail risk contagion. Furthermore, the COVID-19 pandemic has a continuous impact on the cryptocurrency market. When financial institutions are increasingly investing in crypto assets, the hidden risks in the cryptocurrency market remain high. Therefore, this paper calls for attention on the cryptocurrency market from both investors and regulators.

Currency dominance has been the symbol of national power, influence, and dominance. After the Second World War, the Dollar has maintained its unrivaled influence as a currency reserve by central banks and as a global transaction currency. Recently, cryptocurrency and the distributed ledger system were seen as a challenge. However, due to the challenge it poses to the sovereignty of nation-states, central banks have resorted to developing the central bank digital currencies (CBDCs). China is the only major economy to have tested a CBDC, a symbol of its increasing economic power and innovation. Contrary to Mearsheimer’s theory of offensive realism, developments show that China can use its offensive economic capabilities to build a regional order through the belt and road initiative (BRI). With the recent release of its Central Bank Electronic Payment and the Blockchain-based Network System, China can seek to regionalize the use of its renminbi (RMB) and rival the power of the Dollar.

We analyze fintechs and their impact on the traditional financial system from a functional perspective. Following the approach suggested by Merton (1995) [A Functional Perspective of Financial Intermediation, Financial Management 24(2), 23–41], we show how the six core functions of financial intermediation are affected by the technological developments. This analysis provides a new perspective on the future of financial services and their regulation.

In this chapter, we outline a theory of cryptocurrencies that parallels the standard theory of money. We evidence that cryptocurrencies satisfy some but not all conditions that qualify a medium of exchange as money. Specifically, the process of creation and distribution of cryptocurrencies significantly differs from that of money impacting trust and value creation. New form of cryptocurrencies, such as central bank digital currencies, are considered. We outline scenarios of future evolution of cryptocurrencies and how they might be adopted by central banks to replace cash and/or to have direct interaction with the public.

In this chapter, the cross-correlations of cryptocurrency returns are analyzed. The chapter examines one years worth of data for 146 cryptocurrencies from the period 01/01/2019 to 31/12/2019. The cross-correlations of these returns are firstly analyzed by comparing eigenvalues and eigenvector components of the cross-correlation matrix C with random matrix theory (RMT) assumptions. Results show that C deviates from these assumptions indicating that C contains genuine information about the correlations between the different cryptocurrencies. From here, Louvain community detection method is applied as a clustering mechanism and 15 community groupings are detected. Finally, Principal Component analysis (PCA) is completed on the standardized returns of each of these clusters to create a portfolio of cryptocurrencies for investment. This method selects a portfolio which contains a number of high value coins when compared back against their market ranking in the same year. In the interest of assessing continuity of the initial results, the method is also applied to a smaller dataset of the top 50 cryptocurrencies across three time periods of T = 125 days, which produces similar results. The results obtained in this chapter show that these methods could be useful for constructing a portfolio of optimally performing cryptocurrencies.

Decentralized Finance (DeFi) represents an emerging sector within the cryptocurrency space. DeFi is currently one of the most groundbreaking and disruptive technologies impacting upon the centralized finance systems, bringing with it many distinctive features and huge potential. In this chapter, we present stylized facts on DeFi and shed light on the broader empirical features of market efficiency, volatility clustering, leverage effects, and the return volume relationship of this market.

Extending the time period of Goodell and Goutte (2020), we apply wavelet methods to daily data of COVID-19 world deaths and daily Bitcoin prices from December 31, 2019 to August 31, 2020. We confirm Goodell and Goutte (2020) for an extended period by evidencing a positive correlation between levels of COVID-19 and Bitcoin prices, suggesting Bitcoin as a safe haven investment. Investigations such as this are important to both scholars and policy-makers, as well as investment professionals interested in the financial implications of both COVID-19 and cryptocurrencies.

This chapter seeks to contextualize the nature of cryptocurrencies as an alternate form of capital that, while being inspired by cryptoanarchist thought, has come to embody extreme forms of inequality among its owners. The concentration of wealth produces a “whale effect” that, as the chapter argues, in fact reflects the forms of inequalities that are found in the ownership of traditional forms of capital. The chapter thus alludes to the mismatch between the professed cryptoanarchist philosophical bent of cryptocurrency owners and the reality of capital ownership in the cryptocurrency domain; while cryptoanarchism postulates autonomy, decentralization, and the spread of ownership, the whale effect suggests that cryptocurrencies are insufficiently different from traditional forms of capital in this regard. This challenges the degree to which the praxis of cryptocurrencies coheres with the philosophy of cryptoanarchism.

In several countries in the world, Bitcoin and P2P lending have been accepted and developed strongly. This study aims to evaluate the suitability, pros, and cons of Bitcoin, Fintech, P2P lending, and its platform in emerging markets such as Vietnam. The research used qualitative analysis combined with data collection method published, statistics, analysis, synthesis, comparison, to generate qualitative comments and discussion; evaluate results, the article analyzed and evaluated the impacts of Fintech, P2P lending, and Bitcoin and virtual currency on society of Vietnam, both positive and negative sides. It was found that we need to improve regulations on Fintech and shadow banking to overcome the weaknesses of commercial banks, to reduce risks as many nations in the world accept it. Experiences of other countries such as the United States, Japan, China, or the developed countries of the European Union and consequences for the economy became a lesson for well as developing countries. Hence, we need to implement risk management plans to reduce technological and IT risks. Proper solutions and development orientation as well as risk management for Bitcoin and cryptocurrencies are suggested. Last but not least, the research was limited to the case of Vietnam; hence, we can expand research to other Asian countries or other emerging markets.