Purpose The present study sets out to examine the empirical literature on the behavioural aspects of cryptocurrencies, showing the findings of related studies and discussing the various results. A systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important in terms of providing a guide for future research. Key topics include an extent review on the issue of herding behaviour amongst cryptocurrencies, momentum effects and overreaction, contagion effect, sentiment and uncertainty, along with studies related to investment decision-making, optimism bias, disposition, lottery and size effects. Design/methodology/approach Systematic literature review. Findings A systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important in terms of providing a guide for future research. Key topics include an extent review on the issue of herding behaviour amongst cryptocurrencies, momentum effects and overreaction, contagion effect, sentiment (investor's, market's) and uncertainty, along with studies related to investment decision-making, optimism bias, disposition, lottery and size effect. Originality/value The authors' survey paper complements recent papers in the area by offering a systematic account on the influence of behavioural factors on cryptocurrencies. Further, this study's purpose is not just to index the relevant literature, but rather to showcase and pinpoint several research areas that have emerged in the field of behavioural cryptocurrency research. For all these reasons, a systematic literature review of cryptocurrencies in behavioural finance seems to be timely and particularly important.
Examining the effect of behavioural factors, such as investor attention, on cryptocurrency markets is particularly important since, in contrast to traditional assets, they often have little intrinsic value, and so prices cannot be explained by fundamentals. This chapter presents several of the most commonly used proxies for investor attention, incorporating both indirect and direct measures. It then briefly introduces the research regarding the effects of investor attention in the context of stock markets. This provides a framework from which it is possible to understand the mechanism by which investor attention may influence cryptocurrencies. The chapter also discusses the emerging research that specifically relates to investor attention in cryptocurrency markets, including various measures of attention and the impact on returns, liquidity, volatility, and crash risk. The substantial price gains and extreme return volatility exhibited by cryptocurrencies has grabbed the attention of a range of investors, suggesting that investor attention is a particularly important behavioral factor to consider.
Sitara Karim, Brian M. Lucey, Muhammad Abubakr Naeem, Gazi Salah Uddin
The high volatility of the blockchain markets has driven the attention of investors and market participants to concentrate on the diversification avenues of NFTs, DeFi Tokens, and Cryptocurrencies. We examined the extreme risk transmission of blockchain markets using the quantile connectedness technique at the median, extreme low, and extreme high volatility conditions. We find significant risk spillovers among blockchain markets with strong disconnection of NFTs. Meanwhile, time-varying features characterized various uneven economic circumstances. Overall, NFTs offer greater diversification avenues with substantial risk-bearing potential among other blockchain markets to shelter the investments and minimize extreme risks.
Brian M. Lucey, Samuel A. Vigne, Larisa Yarovaya, Yizhi Wang
We have developed and made available a new Cryptocurrency Uncertainty Index (UCRY) based on news coverage. Our UCRY Index captures two types of uncertainty: that of the price of cryptocurrency (UCRY Price) and uncertainty of cryptocurrency policy (UCRY Policy). We show that the constructed index exhibits distinct movements around major events in cryptocurrency space. We suggest that this index captures uncertainty beyond Bitcoin, and can be used for academic, policy, and practice-driven research.
In early 2021, non-fungible tokens (NFT) became the first application of blockchain technology to achieve clear public prominence. NFTs are tradeable rights to digital assets (images, music, videos, virtual creations) where ownership is recorded in smart contracts on a blockchain. Given the NFT market emerged out of cryptocurrencies, we explore if NFT pricing is related to cryptocurrency pricing. A spillover index shows only limited volatility transmission effects between cryptocurrencies and NFTs. But wavelet coherence analysis indicates co-movement between the two sets of markets. This suggests that cryptocurrency pricing behaviours might be of some benefit in understanding NFT pricing patterns. However, the low volatility transmissions also indicate that NFTs can potentially be considered as a low-correlation asset class distinct from cryptocurrencies.
The market for non-fungible tokens (NFTs), transferrable and unique digital assets on public blockchains, has received widespread attention and experienced strong growth since early 2021. This study provides an introduction to NFTs and explores the 14 largest submarkets using data from the Ethereum blockchain between June 2017 and May 2021. The analyses rely on (a) the number of NFT sales, (b) the dollar volume of NFT trades and (c) the number of unique blockchain wallets that traded NFTs. Based on the number of transactions and wallets, the Ethereum-based NFT market peaked at the end of 2017 due to the success of the CryptoKitties project. As of 2021, fewer transactions occur but the traded value is much higher. We find that NFT submarkets are cointegrated and feature various causal short-run connections between them. The success or adoption of younger NFT projects is influenced by that of more established markets. At the same time, the success of newer markets has an impact on the more established projects. The results contribute to the overall understanding of the NFT phenomenon as an emerging asset class and suggest that NFT markets are immature or even inefficient.
The research aimed to evaluate suitability of Bitcoin and its platform in emerging markets such as Vietnam. We 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 Bitcoin and virtual currency on society of Vietnam, both positive and negative sides. It was found that virtual currency not accepted in Vietnam as means of payment yet, while many nations in the world accept it. We need to complete the legal framework for virtual currencies in general, Bitcoin in particular. The State should continue to have policies to improve information technology infrastructure, build and improve the capacity of the contingent of financial experts, encryption, and security experts and give warning risks in virtual currency transactions. The scientific value of paper is using experiences from previous studies in other countries to generate recommendations for conditions of Bitcoin development in merging markets. 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.
Many models have been developed to model, estimate and forecast financial time series volatility, amongst which are the most popular autoregressive conditional heteroscedasticity (ARCH) model introduced by Engle (1982) and generalized autoregressive conditional heteroscedasticity (GARCH) model introduced by Bollerslev (1986). The aim of this paper is to determine which type of ARCH/GARCH models can fit the best following cryptocurrencies: Ethereum, Neo, Ripple, Litecoin, Dash, Zcash and Dogecoin. It is found that the EGARCH model is the best fitted model for Ethereum, Zcash and Neo, PARCH model is the best fitted model for Ripple, while for Litecoin, Dash and Dogecoin it depends on the selected distribution and information criterion.
Klender Aimer Cortéz Alejandro, Martha del Pilar Rodríguez-García, Samuel Mongrut
In this paper, we compare the predictions on the market liquidity in crypto and fiat currencies between two traditional time series methods, the autoregressive moving average (ARMA) and the generalized autoregressive conditional heteroskedasticity (GARCH), and the machine learning algorithm called the k-nearest neighbor (KNN) approach. We measure market liquidity as the log rates of bid-ask spreads in a sample of three cryptocurrencies (Bitcoin, Ethereum, and Ripple) and 16 major fiat currencies from 9 February 2018 to 8 February 2019. We find that the KNN approach is better suited for capturing the market liquidity in a cryptocurrency in the short-term than the ARMA and GARCH models maybe due to the complexity of the microstructure of the market. Considering traditional time series models, we find that ARMA models perform well when estimating the liquidity of fiat currencies in developed markets, whereas GARCH models do the same for fiat currencies in emerging markets. Nevertheless, our results show that the KNN approach can better predict the log rates of the bid-ask spreads of crypto and fiat currencies than ARMA and GARCH models.
Abstract. This research employs Capital Asset Pricing Model and foreign exchange exposure theory to explain how the value of financial stocks is affected by the home country cryptocurrency. Previous literature proposed that financial stocks were related to the economic or individual financial ratio, but rarely discussed the impact of a cryptocurrency variable in the digital economy. This paper presents specific findings to prove that cryptocurrency development causes structural change in the financial industry, by examining 67,166 panel data observations from China and Taiwan markets. We offer the following important conclusions: 1. Financial stocks in the China market suffer significantly higher impacts from home country cryptocurrency exposure than the Taiwan market. 2. Financial stocks in the China market are more greatly shocked by the CAPM three factors variables than the Taiwan market. 3. There are significant differences between the two financial markets. 4. The dynamics of the adjustment process of cryptocurrency evolution and the monetary system are key solutions for both markets. Keywords: cryptocurrency, Fin-Tech, Exchange rate Exposure. JEL Classification A14, D82, F65, G12, F3 Formulas: 2; fig.: 0; tabl.: 4; bibl. 31.
Calendar anomalies as the seasonal tendencies in stock returns are the signal of irregular behaviour of stock markets. These anomalies have been comprehensively studied in many matured as well as emerging stock markets. But there is lack of exploration of calendar anomalies in the cryptocurrency market. So, the present treatise is an attempt to fill this lacuna by studying day of the week effect on cryptocurrencies' returns and volatility. This study is based on the prices of eight cryptocurrencies (viz. Bitcoin, EOS, Ethereum, Bitcoin Cash, Litecoin, Tether, XRP and Stellar) for a period starting from July 2017 and up to March 2020. The series of daily and day-wise returns were initially studied for stationarity using Ng-Perron tests and augmented Dickey–Fuller test. The results from these tests confirmed that the cryptocurrencies' return series are stationary. The day of the week effect on cryptocurrencies returns was studied by introducing the dummies for each day of the week in the ordinary least square regression equation. The residuals from the ordinary least square regression equation were tested for ARCH effect using Engle's ARCH test. The results from the test confirmed the presence of ARCH effect in all series. The GARCH (1,1) model and PARCH model were further applied to account for ARCH effect and these models confirmed the presence of the day of the week effect in all the cryptocurrencies' returns and volatility except for day of week effect in Bitcoin and Tether returns. So, the significant day of the week effect was present in all cryptocurrencies' returns and volatility but the significant day of the week effect was absent in Bitcoin's returns and Tether's returns. These findings of significant day effect may help the existing and potential investors in taking investment decision in contemporary scenario of no ban in cryptocurrency market in India.
Risk in finance may come from (negative) asset returns whilst payment loss is a typical risk in insurance. It is often that we encounter several risks, in practice, instead of single risk. In this paper, we construct a dependence modeling for financial risks and form a portfolio risk of cryptocurrencies. The marginal risk model is assumed to follow a heteroscedastic process of GARCH(1,1) model. The dependence structure is presented through vine copula. We carry out numerical analysis of cryptocurrencies returns and compute Value-at-Risk (VaR) forecast along with its accuracy assessed through different backtesting methods. It is found that the VaR forecast of returns, by considering vine copula-based dependence among different returns, has higher forecast accuracy than that of returns under prefect dependence assumption as benchmark. In addition, through vine copula, the aggregate VaR forecast has not only lower value but also higher accuracy than the simple sum of individual VaR forecasts. This shows that vine copula-based forecasting procedure not only performs better but also provides a well-diversified portfolio.
Andrew Meegan, Shaen Corbet, Charles Larkin, Brian M. Lucey
Blockchain technology appears to be ready to revolutionise a broad number of industries. However, the blockchain itself contains a number of inefficiencies and areas for improvement, namely: transaction fees and transaction speeds. Directed acyclic graphs (DAGs) address, and improve on these inefficiencies and a number of digital currencies utilising this technology have already begun to appear. This paper provides an explanation of the technology behind DAG-based assets, while identifying and highlighting strategic advantages that DAGs possess over traditional blockchains. We conduct an EGARCH volatility analysis of a range of blockchain-based and DAG-based cryptocurrencies in the aftermath of a range of market shocks, taking the form of regulatory announcements such as bans and broad restrictions for cryptocurrencies. We find that DAG-based assets become increasingly responsive to market shocks as they mature. Such behaviour mirrors that of established cryptocurrencies such as Bitcoin, Ethereum and Litecoin, providing evidence that DAG-based cryptocurrencies now share similar characteristics to traditional blockchain-chain based products.
In this paper, we analyze various Decentralized Finance (DeFi) protocols in terms of their token distributions. We propose an iterative mapping process that allows us to split aggregate token holdings from custodial and escrow contracts and assign them to their economic beneficiaries. This method accounts for liquidity-, lending-, and staking-pools, as well as token wrappers, and can be used to break down token holdings, even for high nesting levels. We compute individual address balances for several snapshots and analyze intertemporal distribution changes. In addition, we study reallocation and protocol usage data, and propose wrapping complexity as a proxy for measuring token dependencies and ecosystem integration. The paper offers new insights on DeFi interoperability as well as token ownership distribution and may serve as a foundation for further research.
Cryptocurrencies provide an important dimension of innovation to the evolution of the exchange medium we call money. There are now close to 2,000 such currencies, and their potential and volume is growing. The impact of such currencies for money laundering, law enforcement, and banking supervision have been extensively discussed on the transaction level. But this is the “micro” level of analysis. What has been rare is a “macro” level discussion of the impact on the monetary system of a country. Central banks, which are institutions tasked with providing monetary stability, will see their problems rise while the power of their traditional tools to control money supply and interest rates – such as reserve requirements and the discount rates – is declining. But the new digital technologies – such as distributed ledgers – and new approaches provide regulatory bodies also with new and potentially powerful tools. The task for central banks and policy makers is to create new approaches to use, regulate, and incent them in shaping the macro-economic path of their economy. The paper will propose several of these approaches. This is of particular importance in an economic recovery post coronavirus. In the process, central banks will also, predictably, issue their own digital currencies, and a tiny number of those will become global super-currencies. This will create a new type of issues.
This paper analyzes high-frequency estimates of good and bad realized volatility of Bitcoin. We show that volatility asymmetry depends on the volatility regime and the forecast horizon. For one-day ahead forecasts, good volatility commands a stronger impact on future volatility than bad volatility on average and in extreme volatility regimes but not across all quantiles and volatility regimes. For 7-day ahead forecasting horizons the asymmetry is similar to that observed in stock markets and becomes stronger with increasing volatility. Compared with stock markets, the persistence and predictability of volatility is low indicating high variations of volatility.
The development of cryptocurrency as a means of exchange without legal backing and invisibility of the identity of operators has posed peculiar challenges such as illicit financial flow and terrorism amongst others, to the country. This study, therefore, sought to examine the effect of cryptocurrency on the Nigerian economy. The study was hinged on social exchange theory. Secondary data were obtained from the CBN statistical bulletin and Global Financial Integrity Report for a period of six years from 2013 to 2018. The data were analyzed using a simple regression model. The result shows that R is 5.8% which means that there is a low positive relationship between cryptocurrency and the level of economic development in Nigeria. It further shows an adjusted R square of -24.6 which depicts that cryptocurrency has a low inverse effect on the level of economic development in Nigeria. In conclusion, the computed p-value of 0.913 which is higher than the set p-value of 0.05 shows that cryptocurrency does not have a significant effect on the level of economic development in Nigeria. Hence, it is recommended that, in order to sustain economic development from the activities of cryptocurrency in Nigeria, the Central Bank of Nigeria needs to ensure that laws and mechanisms are put in place to adequately capture the activities of cryptocurrency in the country.
Helder Miguel Correia Virtuoso Sebasti�ã, o N.A., Paulo José Osório Rupino da Cunha, Pedro Manuel Cortes�ã · 5 authors
This paper presents an overview of the main developments of cryptocurrencies and discusses their future perspectives. First, it briefly reviews the history of cryptocurrencies since the creation of Bitcoin, presents the main market trends, and discusses the key features of cryptocurrencies in the context of blockchain. Second, it analyses current cryptocurrency projects, like the Libra project, and other applications of the blockchain technology. Third, it presents a systematic economics and financial literature review on cryptocurrencies. Fourth, it examines the challenges, benefits, and future perspectives of cryptocurrencies and blockchain technology, with a focus on the environmental issues and central bank digital currencies.
A major concern of the adoption and scalability of Blockchain technologies refers to their efficient use for payments. In this work, we analyze how Lightning Network (LN), which represents a relevant infrastructural novelty, is influenced by the market dynamics of its referring cryptocurrency, namely Bitcoin. In so doing, we focus on how the LN is efficient in performing transactions and we relate this feature to the market conditions of Bitcoin. By applying the Toda–Yamamoto variant of Granger-causality, we note that market conditions of Bitcoin do not significantly influence the topological configuration of the LN. Hence, although the LN represents a second layer on the Bitcoin blockchain, our findings suggest that its efficient functioning does not appear to be related to the simple market performance of its underlying cryptocurrency and, in particular, of its volatile market fluctuations. This result may therefore contribute to shed light on the practical usage of the LN as a blockchain technology to favor transactions.
In this study, we conduct a network analysis with centrality measures, using historical daily close prices of top 120 cryptocurrencies between 2013 and 2020, to study and understand the dynamic evolution and characteristics of the cryptocurrency market. Our study has two primary findings: (1) the overall cross-return correlation among the cryptocurrencies is weakening from 2013 to 2016 and then strengthening thereafter; (2) cryptocurrencies that are primarily used for transaction payment, notably BTC, dominate the market until mid-2016, followed by those developed for applications using blockchain as the underlying technology, particularly data storage and recording such as MAID and FCT, between mid-2016 and mid-2017. Since then, ETH, alongside with its strongly correlated cryptocurrencies have replaced BTC to become the benchmark cryptocurrencies. Furthermore, during COVID-19, QTUM and BNB have intermittently replaced ETH to take the leading positions due to their active community engagement during the pandemic.
In this paper, we explore the volatility spillovers across different Bitcoin markets. We decompose the realized volatility into common and idiosyncratic volatilities, as well as the good and bad volatilities. Then the asymmetry in volatility spillovers between Bitcoin markets is measured by the DY (Diebold and Yilmaz) index. In addition, we construct statistics to test the asymmetry in volatility spillovers between different Bitcoin markets. The results are achieved as follows. The spillovers of systematic and idiosyncratic volatilities dominate the connectedness among different Bitcoin markets. In addition, the idiosyncratic volatility spillovers are more easily influenced by policies. Good volatility spillovers dominate the Bitcoin markets and change over time. The further results suggest that there is significant asymmetry between systematic and idiosyncratic volatility spillovers in the Bitcoin markets, while the asymmetries between good and bad volatility spillovers are heterogeneous in different markets. The findings in this paper can provide some suggestions for regulators controlling market stability and investors generating investment strategies.