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4,843 results · page 146 of 202
Son Duy Pham, Thao T.T. Nguyen, Hung Xuan
No abstract is available for this record.
Julapa Jagtiani, Michael G. Papaioannou, George P. Tsetsekos, Erik Dolson · 5 authors
No abstract is available for this record.
Nishant Jagannath, Tudor Barbulescu, Karam M. Sallam, Ibrahim Elgendi · 8 authors
Bitcoin generates a massive amount of data every day due to its innate transparency and capacity of operating completely decentralised. In this paper, we introduce on-chain metrics derived from data on the bitcoin network that enable us to describe the state and usage of the underlying network. Based on their characteristics, we classify them into user, miner, exchange activities and run a correlation analysis with the price to understand the dynamics of bitcoin's price and its underlying mechanics. Using the correlated data, we develop a deep learning model. However, determining the best values of parameters in a deep learning model can be a very challenging and time-consuming task. Hence, we propose a self-adaptive technique using a jSO optimization algorithm to find the best values of these parameters to accurately predict the price of bitcoin. Compared to traditional LSTM model, our approach is highly accurate and optimised with a minimum error rate.
Stefan Scharnowski, Yanghua Shi
No abstract is available for this record.
Lu Yang, Haifeng Xu
The economic risk of the carbon footprint of the Bitcoin network remains unexplored. We develop the real-time artificial price for the carbon footprint of the Bitcoin network and thereby extend the climate value at risk (VaR) into the climate expected shortfall (ES) by employing both parametric and semiparametric models. On the basis of the best-fitted climate VaR and ES estimations, we find that the 95th percentiles (upper bound) of the climate VaR and ES are 8.04 and 10.37 billion euros, respectively, and the 99th percentiles (upper bound) of climate VaR and ES are 11.33 and 14.15 billion euros, respectively. Moreover, given the climate VaR and ES estimations on the basis of similar carbon footprint, the negative environmental externality of the Bitcoin network based on the current carbon price is not sufficient to reflect the environmental cost. Overall, our research provides new insight into the linkage between the Bitcoin network and the environment, which will provide meaningful information for both investors and policymakers.
Min Shu, Ruiqiang Song, Wei Zhu
In this study, the Log-Periodic Power Law Singularity (LPPLS) model is adopted for real-time identification and monitoring of Bitcoin bubbles and crashes using different time scale data, and the modified Lagrange regularization method is proposed to alleviate the impact of potential LPPLS model over-fitting to better estimate bubble start time and market regime change. The goal here is to determine the nature of the bubbles and crashes (i.e., whether they are endogenous due to their own price evolution or exogenous due to external market and/or policy influences). A systematic market event analysis is performed and correlated to the Bitcoin bubbles detected. Based on the daily LPPLS confidence indictor from 1 December 2019 to 24 June 2021, this analysis has disclosed that the Bitcoin boom from November 2020 to mid-January 2021 is an endogenous bubble, stemming from the self-reinforcement of cooperative herding and imitative behaviors of market players, while the price spike from mid-January 2021 to mid-April 2021 is likely an exogenous bubble driven by extrinsic events including a series of large-scale acquisitions and adoptions by well-known institutions such as Visa and Tesla. Finally, the utilities of multi-resolution LPPLS analysis in revealing both short-term changes and long-term states have also been demonstrated in this study.
Alexander GuzmĂĄn, Cristian PintoâGutiĂ©rrez, MarĂa Andrea Trujillo DĂĄvila
This paper examines the impact of COVID-19 lockdowns on Bitcoin trading volume. Using data from Apple mobility trends and several time-series econometric models, we find that investors became active participants during the COVID-19 pandemic period and traded more bitcoins on days with low mobility associated with lockdown mandates. These results remain robust after controlling for stocks and gold returns, the VIX index, and the level of attention and sentiment toward Bitcoin, as measured by Google search frequencies and the tone of Tweets discussing Bitcoin. These results suggest that when individual investors have ample free time on their hands, they trade cryptocurrencies as a pastime and use the Bitcoin market as a form of entertainment. Moreover, our results have important implications concerning investorsâ herding behavior and overconfidence leading to noise trader risks and bubbles typically accompanied by high trading volume in cryptocurrency markets.
Lee A. Smales
Abstract We examine the association of Bitcoin, and other cryptocurrency, returns with changes in inflation expectations, and form a comparison with gold, a traditional inflation hedge. We control for uncertainty in economic policy, cryptocurrency, and financial markets, and show that cryptocurrency returns are positively related to changes in US inflation expectations only for a limited set of circumstances. Unlike with gold, the identified relationship is only significant for shortâterm inflation expectations, and when inflation or marketâimplied inflation expectations are below 2% (the Fed's inflation target). Moreover, cryptocurrency returns tend to be lower on days with monthly consumer price index (CPI) announcements and respond negatively to CPI surprises. Our results suggest that cryptocurrencies do not currently offer investors a viable alternative to gold for hedging inflation.
Emmanuel AsafoâAdjei, Peterson Owusu, Anokye M. Adam
The world has witnessed the adverse impact of the COVIDâ19 pandemic. Accordingly, it is expected that information transmission between equities and digital assets has been altered due to the hostile impact of the pandemic outbreak on financial markets. As a result, the ensuing perverse risk among markets is presumed to rise during severe uncertainties occasioned by the COVIDâ19 pandemic. The impetus of this study is to examine the degree of asymmetry and nonlinear directional causality between global equities and cryptocurrencies in the frequency domain. Hence, we employ both the variational mode decomposition (VMD) and the RĂ©nyi effective transfer entropy techniques. Analyses of the study are presented for three sample periods; these are the full sample period, the preâCOVIDâ19 period, and the COVIDâ19 pandemic period. We gauge a mixture of asymmetric and nonlinear bidirectional and unidirectional causality between global equities and cryptocurrencies for the sample periods. However, the COVIDâ19 pandemic period appears to be driving the estimates for the full sample period, which indicates a negative flow. Thus, the direction and significance of the information flow between the markets for the full sample correspond to the one observed during the COVIDâ19 pandemic period. We, consequently, establish a significant directional, dynamical, and scaleâdependent information flow between global equities and cryptocurrencies. Notwithstanding, throughout the study samples, we mainly find a negative significant information flow from global equities to cryptocurrencies. We detect that most cryptocurrencies exhibit similar behaviour of information flow to global equities for each of the sample periods. The outcome provides pertinent signals to investors with diverse investment horizons who would want to diversify, hedge, or employ cryptocurrencies as a safe haven for global equities during uncertainties, specifically the COVIDâ19 pandemic.
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.
Mehmet Balcılar, HĂŒseyin Ăzdemir, BĂŒĆra AÄan
No abstract is available for this record.
Linh Pham, Toan Luu Duc Huynh, Waqas Hanif
No abstract is available for this record.
Jackie Johnson
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.
Dirk G. Baur, Lai T. Hoang, Md Zakir Hossain
No abstract is available for this record.
Peter Zimmerman, Anantha Divakaruni
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.
Chaoqun Ma, Yonggang Tian, Shisong Hsiao, Liurui Deng
No abstract is available for this record.
Cuong Nguyen, Huy Viet Hoang, Duc Khuong Nguyen
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.
Yun Joo An, Paul Moon Sub Choi, Seth H. Huang
No abstract is available for this record.
Akanksha Jalan, Roman Matkovskyy, Larisa Yarovaya
No abstract is available for this record.
Carol Alexander, Daniel F. Heck, Andreas Kaeck
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.
Paraskevi Katsiampa, Larisa Yarovaya, Damian ZiÄba
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.
Mieszko Mazur
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.
George Milunovich
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.