Rabaa Karaa, Skander Slim, John W. Goodell, Abhinav Goyal · 5 authors
We empirically examine whether feedback traders are active in the Bitcoin and the extent to which their presence is affected by a series of noise-related factors (sentiment; volume; liquidity) at three different frequencies (hourly; daily; weekly) for the April 2013âJuly 2019 period based on Bitstamp data. Our findings suggest that positive feedback trading grows stronger for higher (hourly; daily) frequencies, with its presence manifesting itself mainly during periods of high/improving sentiment and high/rising volume/liquidity. Additional tests reveal that the significance of hourly feedback trading is identified during hours corresponding to the trading hours of major European/North American markets. Overall, our results confirm extant literature evidence on the prevalence of noise trading in cryptocurrencies, while further showcasing that the factors motivating feedback trading in other asset classes (equities; ETFs; futures) exhibit similar effects over the presence of feedback traders in the cryptocurrency market.
Cardano was launched in October 2017 and by May 2021 has been operational for 44 months. Comparison with its closest rival, Ethereum, reveals that their prices are highly correlated but the change in daily closing prices do not always move in unison. Cardano is also more volatile than Ethereum and In terms of growth Cardano is lagging behind. Cardanoâs only saving grace is its transaction fees, which are considerably lower than Ethereum. However, care must be taken in understanding the structure of any data source. In this case three data sources are used and results vary depending on the precision of the price data, particularly Cardano which for a number of years did not trade above one dollar.
Carol Alexander, Jun Deng, Jianfen Feng, Huning Wan
Bitcoin prices are driven by upward as well as downward jumps and so the bitcoin implied volatility surface behaves differently from those of established options markets. We analyze tick-level Deribit option price data, demonstrating increasing support for the limits-to-arbitrage hypothesis. Hence market makers are managing order imbalance and inventory more effectively as Deribit bitcoin options trading volumes increases. On the demand side, volatility traders drive both at-the-money and out-of-the-money option prices, the latter also being driven by directional traders. Directional effects were most pronounced during the price bubble of 2021. Further refinements of our tests assess time-to-maturity and time-of-day effects.
In April 2020, the US government sent economic impact payments (EIPs) directly to households, as part of its measures to address the COVID-19 pandemic. We characterize these stimulus checks as a wealth shock for households and examine their effect on retail trading in Bitcoin. We find a significant increase in Bitcoin buy trades for the modal EIP amount of $1,200. The rise in Bitcoin trading is highest among individuals without families and at exchanges catering to nonprofessional investors. We estimate that the EIP program has a significant but modest effect on the US dollarâBitcoin trading pair, increasing trade volume by about 3.8 percent. Trades associated with the EIPs result in a slight rise in the price of Bitcoin of 7 basis points. Nonetheless, the increase in trading is small compared to the size of the stimulus check program, representing only 0.02 percent of all EIP dollars. We repeat our analysis for other countries with similar stimulus programs and find an increase in Bitcoin buy trades in these currencies. Our findings highlight how wealth shocks affect retail trading.
The paper uncovers the volatility spillovers in tails between non-fungible token (NFT) markets and bitcoin from 2019 to 2021. The results show that the NFTs and bitcoin are less connected in the left tail (bearish markets) in comparison to the right tail. It reveals that the Art NFT market has the highest influence in the left tail, while the blockchain game market is the strongest volatility transmitter in the right tail. Notably, the volatility of bitcoin does not play an important role in the NFTs during the study period, and the Metaverse and blockchain game markets have potential portfolio diversification benefit with bitcoin. The findings provide valuable implications for investment portfolio management and policymaking in the transition to the future digital economy.
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.
The risk and return characteristics of NFT-based companies listed on the cryptocurrency market are investigated in this study. The current spike in NFT activity among creators, investors, and traders has prompted our inquiry. The study start by proposing a new classification system for existing NFTs, ranging from NFT blockchains to NFT metaverses to NFT. Next, the study show that NFTs. Earn 130 percent on the first day of trading; Have a long-term investment multiple of 40 (approximately 4,000 percent), which is four times larger than bitcoin; and have positive and strong alpha and above-average beta. The research also show that following the mid-2021 meltdown, the NFT portion of the cryptocurrency market leads market recovery and generates a return of about 350 percent. The paper's conclusion is that integrating NFT infrastructure into current blockchains increases the market value of these networks. Keywords: NFT, Cryptocurrency, Blockchain, Digital Currency, Binance.
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.
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 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.
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 âblack/dirtyâ and âgreen/cleanâ 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.