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This paper aims to investigate feedback trading and autocorrelation behavior in the cryptocurrency market.

It uses the GJR-GARCH model to investigate feedback trading in the cryptocurrency market.

The findings show a negative relationship between trading volume and autocorrelation in the cryptocurrency market. The GJR-GARCH model shows that only the USD Coin and Binance USD show an asymmetric effect or leverage effect. Interestingly, other cryptocurrencies such as Ethereum, Binance Coin, Ripple, Solana, Cardano and Bitcoin Cash show the opposite behavior of the leverage effect. The findings of the GJR-GARCH model also show positive feedback trading for USD Coin, Binance USD, Ripple, Solana and Bitcoin Cash and negative feedback trading for Ethereum and Cardano only.

This paper contributes to the literature by extending Sentana and Wadhwani (1992) to explore the presence of feedback trading in the cryptocurrency market using a sample of the most active cryptocurrencies other than Bitcoin, namely, Ethereum, USD coin, Binance Coin, Binance USD, Ripple, Cardano, Solana and Bitcoin Cash.

This study aims to empirically investigate the linkages between digital trails of social signals (content and profile features of bitcoin-related tweets) and bitcoin price return using a VAR-BEKK-GARCH model.

Bitcoin-related tweets were collected every hour for six months from September 1, 2020, to February 29, 2021. The analysis involved two steps: first, examining tweet content, profiles, sentiment and emotions; and second, investigating the relationship between social signal volatility and hourly bitcoin price return.

Results indicate that bitcoin price changes can impact the sentiment expressed in tweets about bitcoin, and vice versa. While sadness exhibits a bidirectional volatility spillover with bitcoin, fear and anger display a one-period lag. Quartile analyses reveal that only fear in the second quartile shows a bidirectional spillover effect with bitcoin, while all other emotions except sadness demonstrate a unidirectional spillover effect in all remaining quartiles.

The study uses a novel two-step approach to analyze volatility spillovers between social signals and bitcoin price returns. Findings can guide investors and portfolio managers in making better allocation decisions and assist policymakers and regulators in reducing the adverse effects of bitcoin’s volatility on financial system stability.

This paper aims to investigate the effects of volatility transmission among Bitcoin and other leading cryptocurrencies, namely, Binance USD, BNB, Cardano, Dogecoin, Ethereum, Polkadot, Polygon, Solana, Tether, USD Coin and XRP.

The multivariate BEKK-GARCH model is used with the daily data set from 1 January 2017 to 31 March 2023. The data set is analysed in its entirety and is also the COVID-19 epidemic period.

The study reveals that while the volatility of cryptocurrency prices is influenced by their own historical shocks and volatility, there is proof of the effects shock transmission among Bitcoin and other notable cryptocurrencies. Furthermore, the authors identify the spillover effects of volatility among all 11 pairs and provide evidence that conditional correlations with varying time constants are present, and predominantly positive for both the entire and COVID-19 outbreak periods.

The findings will be helpful to market experts who want to avoid losses in traditional assets. To develop the best risk management and hedging strategies, businesses might use the information to build asset portfolios or personalise payment methods. The use of such data by investors and portfolio managers could aid in the development of investment opportunities, risk insurance plans or hedging strategies for the management of financial portfolios.

To the best of the authors’ knowledge, the use of the BEKK-GARCH model for examining the effects of volatility spillover among Bitcoin and the other eleven top cryptocurrencies has not been previously documented.

This article aims to uncover the impact of Google Trends on cryptocurrency markets beyond Bitcoin during the time of increased attention to altcoins, especially during the COVID-19 pandemic.

This paper analyses the nexus among the Google Trends and six cryptocurrencies, namely Bitcoin, New Economy Movement (NEM), Dash, Ethereum, Ripple and Litecoin by utilizing the causality-in-quantiles technique on data comprised of the years January 2016–March 2021.

The findings show that Google Trends cause the Litecoin, Bitcoin, Ripple, Ethereum and NEM prices at majority of the quantiles except for Dash.

The findings will help investors to develop more in-depth understanding of impact of Google Trends on cryptocurrency prices and build successful trading strategies in a more matured digital assets ecosystem.

Without theoretically specifying the future of money as an equivalent commodity of other commodities, it is impossible to reveal the recent role of the emergence of cryptocurrencies, as a reflection of speculative competition increasingly sophisticated in its technological aspect and in response to the abusive use of the spurious competition of the big banks promoting the huge financial bubbles that have haunted the world economy, such as the one unleashed from Wall Street in 2008. The explosive growth of transactions in cryptocurrencies may mean, at some point, in the capitalist economic cycle, the possibility of a new financial bubble, as well as the emergence of new swindles to investors; but valid answers can also come from those actors who until now have had to endure the almost exclusive dominance of the international monetary system by the currency issued by the US government, the main exporter of inflation on a global scale.

The current paper proposes a prediction model for a cryptocurrency that encompasses three properties observed in the markets for cryptocurrencies—namely high volatility, illiquidity, and regime shifts. As far as the authors’ knowledge extends, this paper is the first attempt to introduce a stochastic differential equation (SDE) for pricing cryptocurrencies while explicitly integrating the mentioned three significant stylized facts.

Cryptocurrencies are increasingly utilized by investors and financial institutions worldwide as an alternative means of exchange. To the authors’ best knowledge, there is no SDE in the literature that can be used for representing and evaluating the data-generating process for the price of a cryptocurrency.

By using Ito calculus, the authors provide a solution for the suggested SDE along with mathematical proof. Numerical simulations are performed and compared to the real data, which seems to capture the dynamics of the price path of two main cryptocurrencies in the real markets.

The stochastic differential model that is introduced and solved in this article is expected to be useful for the pricing of cryptocurrencies in situations of high volatility combined with structural changes and illiquidity. These attributes are apparent in the real markets for cryptocurrencies; therefore, accounting explicitly for these underlying characteristics is a necessary condition for accurate evaluation of cryptocurrencies.

Given the cryptocurrency market boom in recent years, this study aims to identify the factors influencing cryptocurrency pricing and the major gaps for future research.

A systematic literature review was undertaken. Three databases, Scopus, Web of Science and EBSCOhost, were used for this review. The final analysis comprised 88 articles that met the eligibility criteria.

The influential factors were identified and categorized as supply and demand, technology, economics, market volatility, investors’ attributes and social media. This review provides a comprehensive and consolidated view of cryptocurrency pricing and maps the significant influential factors.

This paper is the first to systematically and comprehensively review the relevant literature on cryptocurrency to identify the factors of pricing fluctuation. This research contributes to cryptocurrency research as well as to consumer behaviors and marketing discipline in broad.

Cryptocurrency arose, and grew in popularity, following the financial crisis of 2008 built upon a promise of decentralizing money and payments. An examination of the history of money and banking in the United States demonstrates that stable money benefits from strict controls and commitments by a centralized government through chartering restrictions and a broad safety net, rather than decentralization. In addition, financial crises happen when the government allows money creation to occur outside of official channels. The US central bank is then forced into a policy of supporting a range of money-like assets in order to maintain a grip on monetary policy and some semblance of financial stability.

In addition, this chapter argues that cryptocurrency as a form of shadow money shares many of the problematic attributes of both the privately issued bank notes that created instability during the “free banking” era and the “shadow banking” activities that contributed to the 2008 crisis. In this sense, rather than being a novel and disruptive idea, cryptocurrency replicates many of the systemically destabilizing aspects of privately issued money and money-like instruments.

This chapter proposes that, rather than allowing a new, digital “free banking” era to emerge, there are better alternatives. Specifically, it argues that the Federal Reserve (Fed) should use its tools to improve public payment systems, enact robust utility-like regulations for private digital currencies and limit the likelihood of bubbles using prudential measures.

Criminals have quickly discovered the advantage of crypto assets, with its pseudo-anonymity, untraceability and the ability to freely exchange crypto assets across borders, which makes it an ideal tool for money laundering activities. Switzerland has a technology-neutral framework, and crypto assets are regulated by the existing anti-money laundering (AML) legislation. The purpose of this paper is to gain insights into the industry adoption of measurements to prevent money laundering through crypto assets and if they are compliant with national and international AML regulations.

Semi-structured expert interviews were conducted with participants having expertise in compliance, AML and crypto assets with focus on Switzerland. The interviews were analyzed using the thematic analysis.

The experts have a general consensus that Switzerland is a pioneer when it comes to regulating crypto assets. It is perceived that legislations are released without industry consultation and that AML processes for fiat transactions also work for crypto assets, which is not the case. The results show that the industry wants a consortium to fight money laundering in crypto assets in Switzerland. The current measures to identify money laundering are not optimal, yet, it is the best solution and according to national and international regulations the businesses are perceived to be compliant.

This paper offers new insights on the challenges of AML regulations in crypto assets, given the limited information available. It also provides good practice examples for addressing these challenges, benefiting policymakers, regulators and practitioners in the crypto asset ecosystem.

We investigated the association of COVID-19 indicators and economic uncertainty indices on payment-based system cryptocurrency (i.e. Bitcoin, Ripple and Dogecoin) returns.

We used an autoregressive distributed lag (ARDL) model for panel data and performed robustness checks by utilizing a random effect model (REM) and generalized method of moments (GMM). There are 25 most adopted cryptocurrency’s countries and the data spans from 22 March 2021 to 6 May 2022.

This research discovered four findings: (1) the index of COVID-19 vaccine confidence (VCI) recovers the economic and Bitcoin has become more attractive, causing investors to shift their investment from Dogecoin to Bitcoin. However, the VCI was revealed to be insignificant to Ripple; (2) during uncertain times, Bitcoin could perform as a diversifier, while Ripple could behave as a diversifier, safe haven or hedge. Meanwhile, the movement of Dogecoin prices tended to be influenced by public figures’ actions; (3) public opinion on Twitter and government policy changes regarding COVID-19 and economy had a crucial role in investment decision making; and (4) the COVID-19 variants revealed insignificant results to payment-based system cryptocurrency returns.

This study contributed to verifying the vaccine confidence index effect on payment-based system cryptocurrency returns. Also, we further investigated the uncertainty indicators impacting on cryptocurrency returns during the COVID-19 pandemic. Lastly, we utilized the COVID-19 variants as a cryptocurrency returns’ new determinant.