The salience theory of choice under risk shows that investor behavior drives cross-sectional cryptocurrency returns. Investors place too much weight on salient payouts, causing overvaluation of cryptocurrencies with upward salience returns and undervaluation of those with downward salience returns, leading to negative expected returns for the former and positive expected returns for the latter. The salience effect in the cryptocurrency market is more pronounced than in equity markets, making it a significant risk factor for explaining other cross-sectional returns in the cryptocurrency market. Unlike other documented return predictors, the salience theory uniquely contributes to understanding the cryptocurrency market. Video Abstract: https://youtu.be/F8BxhDWW7b4.
This paper discusses the problems of short-term forecasting of cryptocurrency time series using a supervised machine learning (ML) approach. For this goal, we applied two of the most powerful ensemble methods including Random Forests (RF) and Stochastic Gradient Boosting Machine (SGBM). As the dataset was collected from daily close prices of three of the most capitalized coins: Bitcoin (BTC), Ethereum (ETH) and Ripple (XRP), and as features we used past price information and technical indicators (moving average). To check the effectiveness of these models we made an out-of-sample forecast for selected time series by using the one step ahead technique. The accuracy rate of the forecasted prices by using RF and GBM were calculated. The results verify the applicability of the ML ensembles approach for the forecasting of cryptocurrency prices. The out of sample accuracy of short-term prediction daily close prices obtained by the SGBM and RF in terms of Mean Absolut Percentage Error (MAPE) for the three most capitalized cryptocurrencies (BTC, ETH, and XRP) were within 0.92-2.61 %.
Using daily data over the period August 5, 2013 – September 27, 2019, this study investigates the dynamic spillovers between international monetary policies across four major economies (i.e. Eurozone, Japan, UK and US) and three key cryptocurrencies (i.e. Bitcoin, Litecoin and Ripple). In doing so, we apply a Time-Varying Parameter Vector Auto-Regression (TVP-VAR) model, a dynamic connectedness approach and network analysis. The empirical results indicate that cryptocurrency returns and monetary policy spillovers were particularly large when shadow policy rates became negative, moderated during the Fed's ‘tapering process’, and sharpened again more recently as cryptocurrency buoyancy returned. Gross directional spillovers suggest that shadow policy rates have more ‘to give than to receive’, while those from and to cryptocurrency returns are naturally volatile. There is also strong interconnectedness between monetary policy in either the US or the Eurozone and the UK, and between Bitcoin and Litecoin. However, the spillovers across monetary policy and cryptocurrencies tend to be muted. Finally, spillovers were only slightly larger during the Fed's ‘unconventional’ policy compared to the ‘standard’ era, but their composition qualitatively changed over time.
Building on the groundswell of scholarly, practitioner, cultural, and investor interest in non-fungible tokens (NFTs), the aim of this paper is twofold. First, the paper presents early thoughts on the NFT space circa 2021, allowing for an ex-post evaluation of the assessments made at the time. Second, the paper recommends a multidisciplinary research agenda on NFTs, as encapsulated in the edited volume Non-Fungible Tokens (NFTs): Multidisciplinary Perspectives, highlighting the main areas of research promise and avenues of further enquiry. As such, the paper attempts to serve as a form of shorthand-guidebook for researchers interested in the domain of NFTs.
Blockchain technology is becoming increasingly popular because of its applications in various fields. It gives an edge over the traditional centralized methods as it provides decentralization, immutability, integrity, and anonymity. The most popular application of this technology is cryptocurrencies, which showed a massive rise in their popularity and market capitalization in recent years. Individual investors, big institutions, and corporate firms are investing heavily in it. However, the crypto market is less stable than traditional commodity markets. It can be affected by many technical, sentimental, and legal factors, so it is highly volatile, uncertain, and unpredictable. Plenty of research has been done on various cryptocurrencies to forecast accurate prices, but the majority of these approaches can not be applied in real-time. Motivated from the aforementioned discussion, in this paper, we propose a deep-learning-based hybrid model (includes Gated Recurrent Units (GRU) and Long Short Term Memory (LSTM)) to predict the price of Litecoin and Zcash with inter-dependency of the parent coin. The proposed model can be used in real-time scenarios and it is well trained and evaluated using standard data sets. Results illustrate that the proposed model forecasts the prices with high accuracy compared to existing models.
In this paper, we investigate the herding behaviour of two types of cryptocurrencies, referred to as ”black/dirty” and ”green/clean” based on their energy usage levels. Empirical results reveal that herding generally exists only in the dirty cryptocurrency market, and is more significant in down markets. Moreover, we find that clean cryptocurrencies do herd, but with dirty cryptocurrencies, when the two markets are both positive. Our findings are robust across value- and equal-weighted portfolios and provide valuable insights to investors and policy makers.
Elon Musk, one of the richest individuals in the world, is considered a technological visionary and has a social network of over 69 million followers on social media platform Twitter. He regularly uses his social media presence to communicate on various topics, one of which is cryptocurrency, such as Bitcoin or Dogecoin. Using an event study approach, we analyze to what extent Musk’s Twitter activity affects short-term cryptocurrency returns and volume. In other words, we investigate whether cryptocurrency markets exhibit a “Musk Effect”. Based on a sample of 47 cryptocurrency-related Twitter events, we identify significant positive abnormal returns and trading volume following such events. However, we discover that on average, price effects are only significant for Dogecoin-related Tweets but not for Bitcoin. This is because regarding the latter, the significant price effects of positive and negative news cancel each other out, as further classification and analysis of Bitcoin-related tweets reveals. Our study shows the significant impact that the social media activity of influential individuals can have on cryptocurrencies. This suggests a conflict between the ideals of freedom of speech, morals and investor protection.
This paper examines the dynamic connectedness of return- and volatility spillovers among cryptocurrency index (CRIX), Gold, and uncertainty measures. Apart from traditional uncertainty measures, we also consider two novel uncertainty measures: Cryptocurrency Policy Uncertainty and Cryptocurrency Price Uncertainty indices. We observe that cryptocurrency policy uncertainty is the main transmitter of the return spillovers to other variables. In addition, Gold is a net receiver of both the return and the volatility spillovers. These results are valid under bearish, bullish, and normal market conditions. Our findings contribute to the literature considering the spillover effect between cryptocurrencies and other assets and their determinants.
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.
Is clean energy a safe haven for cryptocurrencies, or vice versa? In this paper, we investigate the hedge and safe haven property of a wide range of clean energy indices against two distinct types of cryptocurrencies based on their energy consumption levels, termed “dirty” and “clean”. Statistical evidence shows that clean energy is not a direct hedge for either of types. However, it serves as at least a weak safe haven for both in extreme bearish markets. Moreover, clean energy is more likely to be a safe haven for dirty cryptocurrencies than clean cryptocurrencies during increased uncertainty. We further study the spillover patterns among clean energy, cryptocurrency, stock, and gold markets. Weak connectedness is found between clean energy and cryptocurrencies which implies the potential use of clean energy as a hedge and diversification tool for cryptocurrencies in the future.
Yizhi Wang, Brian M. Lucey, Samuel A. Vigne, Larisa Yarovaya
Purpose (1) A concern often expressed in relation to cryptocurrencies is the environmental impact associated with increasing energy consumption and mining pollution. Controversy remains regarding how environmental attention and public concerns adversely affect cryptocurrency prices. Therefore, the paper aims to introduce the index of cryptocurrency environmental attention (ICEA), which aims to capture the relative extent of media discussions surrounding the environmental impact of cryptocurrencies. (2) The impacts of cryptocurrency environmental attention on long-term macro-financial markets and economic development remain part of undeveloped research fields. Based on these factors, the paper will further examine the effects of the ICEA on financial markets or economic developments. Design/methodology/approach (1) The paper introduces a new index to capture cryptocurrency environmental attention in terms of the cryptocurrency response to major related events through gathering a large amount of news stories around cryptocurrency environmental concerns – i.e. >778.2 million news items from the LexisNexis News & Business database, which can be considered as Big Data – and analysing that rich dataset using variety of quantitative techniques. (2) The vector error correction model (VECM) and structural VECM (SVECM) [impulse response function (IRF), forecast error variance decomposition (FEVD) and historical decomposition (HD)] are useful for characterising the dynamic relationships between ICEA and aggregate economic activities. Findings (1) The paper has developed a new measure of attention to sustainability concerns of cryptocurrency markets' growth, ICEA. (2) ICEA has a significantly positive relationship with the UCRY indices, volatility index (VIX), Brent crude oil (BCO) and Bitcoin. (3) ICEA has a significantly negative relationship with the global economic policy uncertainty (GlobalEPU) and global temperature uncertainty (GTU). Moreover, ICEA has a significantly positive relationship with the industrial production (IP) in the short term, whilst having a significantly negative relationship in the long term. (4) The HD of the ICEA displays higher linkages between environmental attention, Bitcoin and UCRY indices around key events that significantly change the prices of digital assets. Research limitations/implications The ICEA is significant in the analysis of whether cryptocurrency markets are sustainable regarding energy consumption requirements and negative contributions to climate change. Understanding of the broader impacts of cryptocurrency environmental concerns on cryptocurrency market volatility, uncertainty and environmental sustainability should be considered and developed. Moreover, the paper aims to point out future research and policy legislation directions. Notably, the paper poses the question of how cryptocurrency can be made more sustainable and environmentally friendly and how governments' cryptocurrency policies can address the cryptocurrency markets. Practical implications (1) The paper develops a cryptocurrency environmental attention index based on news coverage that captures the extent to which environmental sustainability concerns are discussed in conjunction with cryptocurrencies. (2) The paper empirically investigates the impacts of cryptocurrency environmental attention on other financial or economic variables [cryptocurrency uncertainty (UCRY) indices, Bitcoin, VIX, GlobalEPU, BCO, GTU index and the Organisation for Economic Co-operation and Development IP index]. (3) The paper provides insights into making the most effective use of online databases in the development of new indices for financial research. Social implications Whilst blockchain technology has a number of useful implications and has great potential to transform several industries, issues of high-energy consumption and CO2 pollution regarding cryptocurrency have become some of the main areas of criticism, raising questions about the sustainability of cryptocurrencies. These results are essential for both policy-makers and for academics, since the results highlight an urgent need for research addressing the key issues, such as the growth of carbon produced in the creation of this new digital currency. The results also are important for investors concerned with the ethical implications and environmental impacts of their investment choices. Originality/value (1) The paper provides an efficient new proxy for cryptocurrency and robust empirical evidence for future research concerning the impact of environmental issues on cryptocurrency markets. (2) The study successfully links cryptocurrency environmental attention to the financial markets, economic developments and other volatility and uncertainty measures, which has certain novel implications for the cryptocurrency literature. (3) The empirical findings of the paper offer useful and up-to-date insights for investors, guiding policy-makers, regulators and media, enabling the ICEA to evolve into a barometer in the cryptocurrency era and play a role in, for example, environmental policy development and investment portfolio optimisation.
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.
Bitcoin was conceptualized in response to perceived shortcomings in the monetary and financialsystem, not only related to large financial institutions but also to discretionary decision makingin monetary policy. Using high-frequency data and a weekly proxy VAR model, I study theimpact of monetary policy on Bitcoin. The paper shows that monetary shocks have sizableeffects on Bitcoin prices, but that these differ in sign: a disinflationary monetary tightening bythe ECB lowers valuations - consistent with the notion of Bitcoin as a digital gold -, whereasa Fed tightening increases Bitcoin prices. I document similar differences with respect to cen-tral bank information shocks and explore potential explanations by studying various aspects ofthe Bitcoin ecosystem. Exploiting both differences in Bitcoin valuations across currencies andblockchain transaction data, the paper shows that the increased demand for Bitcoin following aUS monetary tightening is primarily driven by emerging markets. I argue that this likely reflectsthe technological and institutional particularities of Bitcoin that make it sought after as globaldigital cashwhen international economic and financial conditions deteriorate.
Ahmed H. Elsayed, Giray Gözgör, Chi Keung Marco Lau
This paper examines return and volatility connectedness between Bitcoin, traditional financial assets (Crude Oil, Gold, Stocks, Bonds, and the United States Dollar-USD), and major global uncertainty measures (the Economic Policy Uncertainty-EPU, the Twitter-based Economic Uncertainty-TEU, and the Volatility Index-VIX) from April 29, 2013, to June 30, 2020. To this end, the Time-Varying Parameter Vector Autoregression (TVP-VAR) model, dynamic connectedness approaches, and network analyses are used. The results indicate that total spillover indices reached unprecedented levels during COVID-19 and have remained high since then. The evidence also confirms the high return and volatility spillovers across markets during the COVID-19 era. Regarding the return spillovers, Gold is the centre of the system and demonstrates the safe heaven properties. Bitcoin is a net transmitter of volatility spillovers to other markets, particularly during the COVID-19 period. Furthermore, the causality-in-variance Lagrange Multiplier (LM) and the Fourier LM tests' results confirm a unidirectional volatility transmission from Bitcoin to Gold, Stocks, Bonds, the VIX and Crude Oil. Interestingly the EPU is the only global factor that causes higher volatility in Bitcoin. Several potential implications of the results are also discussed.
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.
The study explores whether blockchain technology can change the paradigm of the current financial structure and the balance of power in the international financial system. Accordingly, this study reviews the development of blockchain technology by analyzing China and Venezuela, both of which struggle to harness their technological advancement and to enhance their power in the international realm. We found that Venezuela invests in blockchain technology to create an alternative payment structure for survivability, while China’s desire is to become a global leader in global blockchain technology.
This paper uses the cross-sectional absolute deviation (CSAD) in static and time-varying versions to examine herding in the cryptocurrency market from April 2013 to November 2019. Results from the static model confirm the evidence of an anti-herding behavior over the considered period. However, the time-varying analysis suggests the presence of herding behavior around the end of 2013 and persists until the end of the sample period. Furthermore, by examining the factors relating to market microstructure and general economic conditions that can drive herding, we find that the level of herding in the cryptocurrency market rises as volatility, the S&P500, and the dollar index increase. However, the rise in the trading volume, gold price, and the economic policy uncertainty index (EPU) reduce the herding in the cryptocurrency market.