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.
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.
This study explores the existing systemic barriers to intra-BRICS national currency use (“de-dollarization”) in currency swaps and trade finance. The author examines the current de-dollarization initiatives, as well as the actual levels of de-dollarization in Russia’s intra-BRICS settlements (as a representative sample), to find gaps between de-dollarization goals and current initiatives and offers a near-term phased solution to overcome these gaps and de-risk trade within BRICS. It is found that 1) the New Development Bank’s Contingency Reserve Arrangement has built-in systemic barriers which are preventing direct currency swaps between BRICS member states; 2) the Euro is replacing the Dollar as Russia’s preferred settlement currency within BRICS, indicating a gap between Russian traders’ settlement currency choice and BRICS de-dollarization priorities; and, furthermore, 3) while payment and settlement systems are being integrated and FinTech applications are being explored, efforts to fundamentally address the systemic market factors preventing national settlement use are missing. A phased solution is proposed to address the fundamental market barriers to national currencies by using smart contracts to de-risk intra-BRICS trade. Specific mechanisms are outlined to promote trade contracts in national currency and reduce dependency on both the Dollar/Euro and Western institutions (such as the IMF and Western commodities markets), a high-level architecture is proposed, and implementation considerations are discussed.
In recent years, there has been a growing interest on the combination of copulas with mixture model. The combination of vine copulas incorporated into a finite mixture model is also helpful to capture secret structures in a data. This paper aims to examine the relationship between bitcoin and other crypto money indicators with the CD Vine Copula Approach method. In the study, we use closing prices of Bitcoin, Bitcoin Cash, Ethereum, Litecoin, and IOT. The results show that there is a strong dependence between bitcoin and prominent financial indicators.
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.
Bu çalışmada amaç; Bitcoin, döviz kuru, Borsa İstanbul Endeksi ve faiz değişkenleri arasındaki ilişkileri Türkiye için 2013:11-2019:10 dönemi haftalık verileri kullanarak incelemektir. Çalışmada VAR modeli kurularak değişkenler arasındaki uzun dönem ve nedensellik ilişkileri araştırılmış, etki-tepki grafikleri ve varyans ayrışım tablosuyla analiz sonuçlandırılmıştır. Çalışma sonucunda Bitcoin ile diğer değişkenler arasında uzun dönemde herhangi bir eşbütünleşme ilişkisi ve nedensellik ilişkisi tespit edilememiş, ancak diğer değişkenlerin kendi aralarında nedensellik ilişkileri saptanmıştır. Etki-tepki grafiklerine göre Bitcoin’e verilen bir şoka döviz kuru üç haftalık negatif tepki göstermiş diğer haftalarda verilen tepki anlamsız olmuştur. Türkiye’de kripto paralar üzerinde belirli bir farkındalığın olduğu ancak bu farkındalığın uzun vadeli yatırım boyutunda ve makro değişkenleri etkileyebilecek güçte olmadığı görülmektedir. Türkiye’de yeni sayılabilecek olan bu teknolojinin yaygınlaşabilmesi için belirli bir zamana ihtiyaç vardır.
Çalışmanın amacı; kripto para birimlerinden Bitcoin ve Litecoin piyasalarının etkinliğini ölçerek haftanın günü etkisinin varlığını 29.04.2013- 29.02.2020 tarihleri arasında günlük kapanış fiyatları kullanılarak incelenmesidir. İlgili dönemlerde her iki para birimine ait piyasaların etkinliğini incelemede ARMA, haftanın günü etkisinin olup olmadığının tespitinde ise Kruskal Wallis H testinden faydalanılmıştır. Çalışmanın sonunda her iki kripto para biriminin getirilerinin bir önceki zamandan bağımsız hareket ettiği yani ilgili dönemde bu kripto para piyasalarının etkin piyasaya benzer özellik taşıdığı ve haftanın günü etkisinin de varlığına rastlanılmadığı tespit edilmektedir.
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.