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Design/Methodology/Approach: This chapter applies tests associated with the adaptive market hypothesis (AMH) and Johansen cointegration test. AMH acknowledges the views of the efficient market hypothesis and behavioural finance approach.

Purpose: Cryptocurrencies are considered a new asset class by multiasset portfolio managers. Hence, we examine the AMH and cointegration in the cryptocurrency market to know whether select cryptocurrencies can be diversified.

Findings: We find that cryptocurrencies are efficient and there is a long-run relationship among constituent series, and there is no short-run causality derived from bitcoin, Ethereum and litecoin to bitcoin, while stellar and Dogecoin have short-run causality to bitcoin.

Originality/Value: This chapter is different from the existing one as this is the first study in which the AMH and Johansen cointegration test are applied to check the efficiency and relationship of Bitcoin, Ethereum, and Monero, Stellar, litecoin and Dogecoin.

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.

The COVID-19 pandemic has led to global economic policy uncertainty, which has increased the need to investigate ways to mitigate the uncertainty. This study aims to examine the potential of cryptocurrencies as a hedge and safe haven avenue against economic policy uncertainty.

This study investigates the behavior of the five leading cryptocurrencies in relation to country-level and group-level economic policy uncertainty indices, as measured by the text-based method developed by Baker et al. (The Quarterly Journal of Economics, 2016, 131, 1593–1636). The research covers a broad range of emerging and developed economies from July 2013 to September 2020. The study employs the approach of Narayan et al. (Economic Modelling, 2016, 53, 388–397) to examine the hedging and safe-haven properties of cryptocurrencies.

This study finds that the top cryptocurrencies play a hedging role against economic policy uncertainty, with some exceptions. Additionally, there is evidence to support the idea that cryptocurrencies can serve as a safe haven during the COVID-19 pandemic. As a result, investors may benefit from using cryptocurrencies as a risk-management avenue during times of uncertainty.

This research contributes to the existing literature by testing the cryptocurrencies' hedging and safe haven properties in a new way, by analyzing their lead and lag behaviors using a recent and innovative approach. Additionally, it examines a wide range of emerging and advanced markets, providing insight into the potential of using cryptocurrencies as a risk mitigation avenue.

The purpose of the article is to analyse the use of cryptocurrencies in general and Bitcoin specifically. The majority of academics are aware of the benefits of using cryptocurrencies for trade facilitation, cost reduction, and similar purposes. Peer-to-peer and remittance transactions without compliance requirements have the potential to be transformed and revolutionised by cryptocurrencies; nevertheless, end users must overcome several obstacles relating to security, privacy, and control in order to take use of Bitcoin. The study elaborates on several facets of cryptocurrencies, beginning with their early development, difficulties and dangers, chances, benefits and drawbacks, and prospects. Secondary data has been used for this study like as from government sources, Scopus indexed journal, famous print media. The study find the addressed challenges pertaining to the operational and technological aspects of cryptocurrencies. And how to resolve the modern problem faced while using cryptocurrency. So we conclude that it is difficult to predict the future of cryptocurrencies, as there is still a lot of work to be done, especially in the area of formal rules.

Implications: In this digital era there is a need for cryptocurrency while the whole world is turning into a cashless economy, this will be useful for our common society and have the best use for implication in the business sector, this will stop the paperwork and sustainability, and, even there is threat cybercrime while the use of cryptocurrency will be increased so by data protection and strong security and protection bill or regulation will give the usual and systematic direction for uses and one line development.

This study investigates the diversification benefits of multiple cryptocurrencies and their usefulness as investment assets, individually or combined, in enhancing the performance of a well-diversified portfolio of traditional assets before and during the pandemic COVID-19.

This paper uses two optimization techniques, namely the mean-variance and the maximum Sharpe ratio. The naïve diversification rules are used for comparison. Besides, the Sharpe and the Sortino ratios are used as performance measures.

The results show that cryptocurrencies diversification benefits occur more during the COVID-19 pandemic rather than before it, with the maximum Sharpe ratio portfolio presenting its highest performance. Furthermore, the results suggest that, during COVID-19, the diversification benefits are slightly better when using a combination of cryptocurrencies to an already well-diversified portfolio of traditional assets rather than individual ones. This serves to improve the performance of the maximum Sharpe ratio portfolio, and to some extent, the naïve portfolio. Yet, cryptocurrencies, whether added individually or combined to a well-diversified portfolio of traditional assets, don't fit in the minimum variance portfolio. Besides, the efficient frontier during COVID-19 pandemic dominates the one before COVID-19 pandemic, giving the investor a better risk-return trade-off.

To the best of the author's knowledge, this is the first study that examines the diversification benefits of multiple cryptocurrencies both as individual investments and as additional asset classes, before and during COVID-19 pandemic. The paper covers all analyses performed separately in previous studies, which brings new evidence regarding the potential for cryptocurrencies in portfolio diversification under different portfolio strategies.

Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

The purpose of this research is to primarily understand how crypto traders can use the Bitcoin as a hedge or safe haven asset to reduce their losses from crypto trading. The study also aims to provide insights to crypto investors (portfolio managers) who wish to maintain a crypto portfolio for the medium term and can use the Bitcoin to minimize their losses. The findings of this research can also be used by policymakers and regulators for accommodating the Bitcoin as a medium of exchange, considering its safe haven nature.

This study applies the cross-quantilogram (CQ) approach introduced by Han et al. (2016) to examine the safe-haven property of the Bitcoin against the other selected crypto assets. This method is robust for estimating bivariate volatility spillover between two markets given unusual distributions and extreme observations. The CQ method is capable of calculating the magnitude of the shock from one market to another under different quantiles. Additionally, this method is suitable for fat-tailed distributions. Finally, the method allows anticipating long lags to evaluate the strength of the relationship between two variables in terms of durations and directions simultaneously.

The Bitcoin acts as a weak safe haven asset for a majority of new crypto assets for the entire study period. These results hold even during greed and fear sentiments in the crypto market. The Bitcoin has the ability to protect crypto assets from sharp downturns in the crypto market and hence gives crypto traders some respite when trading in a highly volatile asset class.

This study is the first attempt to show how the Bitcoin can act as a true matriarch/patriarch for crypto assets and protect them during market turmoil. This study presents a clear and concise representation of this relationship via heatmaps constructed from CQ analysis, depicting the quantile dependence association between the Bitcoin and other crypto assets. The uniqueness of this study also lies in the fact that it assesses the protective properties of the Bitcoin not only for the entire sample period but also specifically during periods of greed and fear in the crypto market.

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.

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.