One of the exciting recent developments in decentralized finance (DeFi) has been the development of decentralized cryptocurrency exchanges that can autonomously handle conversion between different cryptocurrencies. Decentralized exchange protocols such as Uniswap, Curve and other types of Automated Market Makers (AMMs) maintain a liquidity pool (LP) of two or more assets constrained to maintain at all times a mathematical relation to each other, defined by a given function or curve. Examples of such functions are the constant-sum and constant-product AMMs. Existing systems however suffer from several challenges. They require external arbitrageurs to restore the price of tokens in the pool to match the market price. Such activities can potentially drain resources from the liquidity pool. In particular, dramatic market price changes can result in low liquidity with respect to one or more of the assets and reduce the total value of the LP. We propose in this work a new approach to constructing the AMM by proposing the idea of dynamic curves. It utilizes input from a market price oracle to modify the mathematical relationship between the assets so that the pool price continuously and automatically adjusts to be identical to the market price. This approach eliminates arbitrage opportunities and, as we show through simulations, maintains liquidity in the LP for all assets and the total value of the LP over a wide range of market prices.
We analyse high-frequency realised volatility dynamics and spillovers in the bitcoin market, focusing on two pairs: bitcoin against the US dollar (the main fiat-crypto pair) and trading bitcoin against tether (the main crypto-crypto pair). We find that the tether-margined perpetual contract on Binance is clearly the main source of volatility, continuously transmitting strong flows to all other instruments and receiving only a little volatility. Moreover, we find that (i) during US trading hours, traders pay more attention and are more reactive to prevailing market conditions when updating their expectations and (ii) the crypto market exhibits a higher interconnectedness when traditional Western stock markets are open. Our results highlight that regulators should not only consider spot exchanges offering bitcoin-fiat trading but also the tether-margined derivatives products available on most unregulated exchanges, most importantly Binance.
In this paper, we analyse co-movements and correlations between Bitcoin and thirty-one of the most-tradable crypto assets using high-frequency data for the period from January 2019 to December 2020. We apply the Diagonal-BEKK model to data from the pre-COVID and COVID-19 periods, and identify significant changes in patterns of co-movements and correlations during the pandemic period. We also employ the Minimum Spanning Tree (MST) and Planar Maximally Filtered Graph (PMFG) methods to study the changes of the crypto asset network structure after the COVID-19 outbreak. While the influential role of Bitcoin in the digital asset ecosystem has been confirmed, our novel findings reveal that due to recent developments in the blockchain ecosystem, crypto assets that can be categorised as dApps and protocols have become more attractive to investors than pure cryptocurrencies.
Major cryptocurrencies such as bitcoin and etherium rely on the computationally expensive and energy inefficient Proof of Work (PoW) consensus mechanism to validate transactions and secure their networks. In response to such concerns digital coins that implement more energy efficient algorithms, e.g. Proof of Stake (PoS), have started to grow in popularity and some PoW based coins are planning to switch to PoS. We investigate linkages and transmission of price shocks across fourteen PoW and PoS/Other powered digital assets. PoW cryptocurrencies appear to be more strongly connected within the network of digital coins than are PoS/Other digital currencies. On average PoW coins export more uncertainty to other cryptocurrencies, while assets in both groups import similar levels of risk. PoS/Other cryptocurrency stakeholders need to be aware of the impact that PoW cryptocurrencies can exert on the riskiness of their assets.
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.
Using daily data over the period August 5, 2013 â 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 âtapering processâ, and sharpened again more recently as cryptocurrency buoyancy returned. Gross directional spillovers suggest that shadow policy rates have more âto give than to receiveâ, 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 âunconventionalâ policy compared to the âstandardâ era, but their composition qualitatively changed over time.
We analyze the market quality of centralized crypto exchanges (CEXs) and decentralized blockchain-based venues (DEXs) using a unique and comprehensive dataset. Focusing on two fundamental aspects, transaction costs and deviations from the no-arbitrage condition, we estimate the causal effect of ``gas fees'' on DEX market quality. We show that these fixed costs impose a significant burden on relatively small trades and cause persistent arbitrage deviations. Conversely, DEXs offer more competitive transaction costs for larger trades, offering a more favorable environment for institutional investors. Furthermore, we provide causal evidence that innovations aimed at enhancing the flexibility of liquidity provision in DEX markets lead to sizeable improvements in market quality.
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.
Ahmed M. Khedr, Ifra Arif, Pravija Raj P V, Magdi ElâBannany · 6 authors
Abstract Cryptocurrencies are decentralized electronic counterparts of governmentâissued money. The first and bestâknown cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machineâlearning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.
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.
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.
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.
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.
Suresh Kumar Oad Rajput, Ishfaque Ahmed Soomro, Najma Ali Soomro
This study introduces a comprehensive Google search volume based Bitcoin Sentiment Index (BSI) and investigates its symmetric and asymmetric association with Bitcoin's returns, volume, and volatility, and with United States Dollar (USD) exchange rates. Our results indicate a positive association of BSI with Bitcoin's returns and volume, but a negative relationship with its return volatility. Besides, Bitcoin's optimistic sentiments have an asymmetric relationship with the USD exchange rate in the short-run, and the Bitcoin price has an asymmetric and negative association with USD in the short-run and long-run. We also found a 35.47% speed of adjustment to long-run equilibrium.