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4,843 results · page 97 of 202
Melisa Ozdamar, Ahmet Ćensoy, Levent Akdeniz
No abstract is available for this record.
John W. Goodell, Shaen Corbet, Miklesh Prasad Yadav, Satish Kumar · 6 authors
No abstract is available for this record.
Alessio Brini, Jimmie Lenz
No abstract is available for this record.
Soudeh Javadi Masoudian, Paras M. Kathuria, Nisha S. Gowda, Talha Ali Khan
Cryptocurrency investment has been on trend for the last many years. Many studies have been done on cryptocurrency price prediction based on their parameters and factors. In this research, we concentrate on bitcoin as one of the well-known cryptocurrencies. In this regard, some machine learning approaches predicate the bitcoin price. Neural Networks and Recurrent Neural Networks seem great in bitcoin price prediction and relatively accurate because of their ability to work on time-series data. In this paper, the survey on the performance of LSTM (Long Short-Term Memory), which is one of the Recurrent Neural Networks and is suitable for time-series problems, has proven it can be used for bitcoin price prediction investigation. In this paper, to fulfil this measurement task, a model has been implemented to train and test the dataset; the last year's data was only used because of the fluctuation of the price in recent years. The result shows that LSTM can predict the price remarkably with acceptable accuracy.
Jens Klose
Abstract This article investigates similarities and differences between gold and four cryptocurrencies (Bitcoin, Ethereum, Bitcoin Cash and Litecoin) with respect to four determinants. To do so, we estimate a system-GARCH-in-mean for the period starting 7/18/2014 at earliest until 7/12/2021. We find that, first, liquidity premia are almost always insignificant for both gold and cryptocurrencies. Second, volatility premia exist in either gold and cryptocurrencies. Third, the response of cryptocurrencies to exchange rate changes is more pronounced than for gold at least if developing countries are included. Fourth, gold exhibits a safe haven status, while cryptocurrencies do not. So according to our results those cannot be seen as a store of value but rather should be seen as speculative assets.
Mobeen Ur Rehman, Paraskevi Katsiampa, Rami Zeitun, Xuan Vinh Vo
This paper investigates the extreme dependence and risk spillovers between Bitcoin and the currencies of the BRICS and G7 economies. We find time-varying dependence between Bitcoin and all currencies. Moreover, when analysing risk spillovers from Bitcoin to currencies, we find that Bitcoin exercises significant power over most currencies, with the South African rand and Brazilian real holding both the highest downside and upside risk before and during the COVID-19 pandemic period, respectively. When considering risk spillovers from currencies towards Bitcoin, the Japanese yen exhibits the highest downside spillovers. Importantly, we find asymmetric spillovers between extreme upward and downward movements.
Xin Liu, Bowen Li
This paper investigates the price and risk dynamics of Bitcoin. Applying SVAR to study Bitcoin, gold and U.S. dollar in one system, we find that neither the gold nor U.S. dollar can explain Bitcoin pricing dynamics in the short-run. We further apply the DCC-MGARCH model to study the risk correlations. The results show that there exists volatility spillover effect and dynamic correlation between three markets, which is magnified with the advent of COVID-19. We can thus draw a conclusion that the boom of Bitcoin is just a hype and speculative bubble.
Seyram Pearl Kumah, Jones OdeiâMensah, Richmell Baaba Amanamah
This paper investigates the co-movement between cryptocurrencies and African stock returns to uncover their degree of association and global portfolio diversification benefits implementing the three-dimensional continuous Morlet wavelet transform technique. Data span 10 August 2015 to 10 December 2021 at daily frequency. The results suggest high degrees of co-movement between the asset markets at medium and lower frequencies implying that stock markets in Africa are highly exposed to cryptocurrency market disruptions from the medium term and that international investors seeking to hedge their price risk in African stock markets using cryptocurrencies may have to look at the short term. The phase difference arrow vectors implying lead (lag) effects are time-varying and heterogeneous showing no particular cryptocurrency or stock market as leader or follower. Different markets have the potential to lead or lag other markets at varying scales which may induce arbitrage opportunities for international and local investors. Our findings provide insights for policymakers, regulators and international investors as an economyâs monetary policy can be affected by the connections between the domestic capital market and other markets globally.
LĂȘ Thanh HĂ
Purpose The authors attempt to explore fat tails and network interlinkages of oil prices and the six largest cryptocurrencies from 1st January 2018 and 1st August 2021. The authors also investigate the influences of the COVID-19 pandemic on these network interlinkages. Design/methodology/approach The authors follow Diebold and Yilmaz (2012) to calculate the spillover index the dynamic correlation coefficient model firstly employed by Engle (2002) to study how the volatility of oil prices are transmitted to those of cryptocurrency return and liquidity and vice versa. Findings The results confirm the presence of time-varying interlinkages between the volatilities of the oil market and the cryptocurrency market. Notably, uncertain events like the COVID-19 health crisis significantly influence the time-varying interlinkages they augment dramatically during the COVID-19 health crisis. The turbulence of the cryptocurrency market, especially from Bitcoin and Ethereum, significantly impacts those of the oil market. The role of the oil market in transmitting the effect of respective shocks to the cryptocurrency market, on the other hand, is time-varying, which is only reported when the COVID-19 pandemic first appeared at the beginning of 2020. The turbulence of the cryptocurrency market in the system is greatly explained by themself rather than a transmission mechanism of shocks to the oil market. Practical implications Insightful knowledge about key antecedents of contagion among these markets also help policymakers design adequate policies to reduce these markets' vulnerabilities and minimize the spread of risk or uncertainty across these markets. Originality/value The most significant benefit of the approach is how simple it is to calculate net pairwise connectivity, which identifies transmission channels between these commodity and financial markets. The authors are also the first to use the quasi-maximum likelihood (QML) estimator to estimate the DCC model to measure the volatility spillover index to reflect the level of interdependence between the different markets. By using a daily and up to date database, the authors can observe the role of each market in transmitting and receiving the shocks between two different sub-periods: (1) before and (2) during the COVID-19 pandemic crisis.
Arav Ouandlous, John Barkoulas, Themis D. Pantos
No abstract is available for this record.
Yufei Xia, Chong Sang, Lingyun He, Ziyao Wang
No abstract is available for this record.
Yizhi Wang, Florian Horky, Lennart John Baals, Brian M. Lucey · 5 authors
Amid surging market values and widespread regulatory discussion, NFT and DeFi markets are widely perceived as being simply speculative in nature. This paper detects the existence and dates of price bubbles in the NFT and DeFi markets by applying SADF and GSADF tests. We document that NFT and DeFi markets both exhibit speculative bubbles, with NFT bubbles being more recurrent and having higher average explosive magnitudes than DeFi bubbles. The price bubbles in the NFT and DeFi markets are highly correlated with market hype and with more general cryptocurrency market uncertainty. We do find periods where bubbles are not detected, suggesting that these markets do have some intrinsic value and should not be dismissed as simply bubbles.
Muhammad Sheraz, Silvia Dedu, Vasile Preda
This paper aims to empirically examine long memory and bi-directional information flow between estimated volatilities of highly volatile time series datasets of five cryptocurrencies. We propose the employment of Garman and Klass (GK), Parkinson's, Rogers and Satchell (RS), and Garman and Klass-Yang and Zhang (GK-YZ), and Open-High-Low-Close (OHLC) volatility estimators to estimate cryptocurrencies' volatilities. The study applies methods such as mutual information, transfer entropy (TE), effective transfer entropy (ETE), and RĂ©nyi transfer entropy (RTE) to quantify the information flow between estimated volatilities. Additionally, Hurst exponent computations examine the existence of long memory in log returns and OHLC volatilities based on simple R/S, corrected R/S, empirical, corrected empirical, and theoretical methods. Our results confirm the long-run dependence and non-linear behavior of all cryptocurrency's log returns and volatilities. In our analysis, TE and ETE estimates are statistically significant for all OHLC estimates. We report the highest information flow from BTC to LTC volatility (RS). Similarly, BNB and XRP share the most prominent information flow between volatilities estimated by GK, Parkinson's, and GK-YZ. The study presents the practicable addition of OHLC volatility estimators for quantifying the information flow and provides an additional choice to compare with other volatility estimators, such as stochastic volatility models.
Ăder Johnson de Area LeĂŁo Pereira, Paulo Ferreira, Derick Quintino
Non-fungible tokens (NFTs) are a type of digital record of ownership used in a unique way: ensuring authenticity and uniqueness. Due to these characteristics, NFTs have been used in several markets: games, arts, and sports, among others. In 2020, the volume of negotiations of the NFTs was about USD 200 million. Despite the strong interest of economic agents in operating with NFTs, there are still gaps in the literature, regarding their dynamics and price interrelation with other potentially related assets, which deserve to be studied. In this sense, the main purpose in this paper is to analyze the cross-correlation between NFTs and larger cryptocurrencies. To this end, our methodological approach is based on a Detrended Cross-Correlation Analysis correlation coefficient, with a sliding windows approach. Our main finding is that the cross-correlations are not significant, except for a few cryptocurrencies, with weak significance at some moments of time. We also carried out an analysis of the long-term memory of NFTs, which demonstrated the antipersistence of these assets, with results seemingly corroborating the market inefficiency hypothesis. Our results are particularly important for different classes of investors, due to the analysis on different time scales.
ELLEKE, OLORUNTOBI CRYSTAL
The global financial system is no doubt embracing the current transition from physical currency to almost virtual currencies through the medium of technology. This tidal wave given rise to virtual currency. Cryptocurrency has been defined as an electronic career high device that uses funds to make note of buying and selling duties and is open to all brokers. The major aim of the study is to examine the integration between the cryptocurrency market and other markets in Africa specifically Nigeria. Secondary source of data was used in this research work. On the one hand, the time series evaluated in this paper consists of the standardized residuals of eleven digital coins by market valuation. The selected cryptocurrencies are Bitcoin, Ethereum, Tether, Bitcoin Cash, Bitcoin SV, XRP, Binance, EOS, Tezos, Cardano, Litecoin, Stellar. On the other hand, data on commodity market of income generation components extends from November 20, 2019 to June 30, 2021. As a result, the nonlinear ARDL cointegration approach (NARDL) was used in this study to predict asymmetries in the short and long run. This methodology is used to test whether the time series are discontinuously associated. It also looks for both short- and long-term inhomogeneity by decomposing the positive and undesirable provisional amounts of the explanatory variables. The results of the analysis confirm that Oil price show the greatest connection with the returns of the cryptocurrencies analyzed. In addition, both short-term and long- term results show a greater interdependence between income generation components and cryptocurrencies. It is therefore noted that eliminating the currency would be seen as unreasonable and unworthy of a country that seeks to promote domestic innovation. Hence, Central Bank of Nigeria should state a proper regulatory action Keywords: Cryptocurrency, Bitcoin, Virtual Currency, Stock Market, Oil Price
JĂșlio LobïżœĂŁo
Green cryptocurrencies have been recently created to reduce energy consumption and environmental pollution by adopting alternative mining practices. This paper examines for the first time the market of green cryptocurrencies for indication of herding behavior in the period of January 2017âJune 2022. By using two measures that capture the proximity of asset returns from the market consensus, we conclude that herding behavior among investors in green cryptocurrencies was absent in the whole sample. However, the results of a subsample analysis and rolling window regression show that herding dynamics varied significantly throughout the sample period. The recent COVID-19 pandemic amplified the observed levels of herding behavior, suggesting that opportunities for diversification for investors operating in this market may have become more limited lately. For this reason, financial regulators should focus on the market of green cryptocurrencies if they want to promote the marketâs efficiency necessary to attract additional investors.
Provash Kumer Sarker, Lei Wang
This study aims to investigate the co-movement and Granger causality between Bitcoin prices (BTC) and M2 (cash, demand, and time deposits), inflation, and economic policy uncertainty (EPU) in the U.K. and Japan. It uses monthly data from 31 July 2010 to 30 August 2020 and employs the wavelet coherence method, Toda-Yamamoto, and nonlinear Granger-causality tests. The empirical results show that (i) Bitcoin prices influence M2 and interact with inflation and EPU. In the short term, inflation affects Bitcoin price positively, supporting Bitcoin as an inflation hedged instrument in Japan. Both in Japan and the U.K., the short-term effects of M2 on Bitcoin prices are negative, while EPU's effects on Bitcoin prices are positive, (ii) a bidirectional Toda-Yamamoto Granger causality exists between Bitcoin prices, inflation, and EPU and confirms that M2 affects Bitcoin prices, (iii) a nonlinear bidirectional causality exists between Bitcoin prices and inflation. While Bitcoin prices Granger cause M2 in the U.K. and Japan, inflation shows a nonlinear Granger causality with EPU in Japan. These findings help investors make investment decisions while considering the effects of M2, inflation, and EPU, and monetary authorities and policymakers make policies involving Bitcoin.
Dongwan Lan, Hao Wang, Changchun Yin, Lu Zhou · 6 authors
Gas is the internal pricing (metering system) for running a contract or in general any transaction in Ethereum. With the popularity of Ethereum, the deficiency of current Ethereum transaction pricing mechanism First Price Auctions is being amplified. The fee paid to miners is the gas used multiplied by the gas price. Hence, designing an effective and accurate gas price prediction method is of great significance for improving the efficiency, transparency and security of the Ethereum transaction mechanism. After the Ethereum âLondonâ Hard Fork update, EIP-1559 has been proposed to change the historical gas mechanism and make transaction fees less volatile and more predictable. Therefore, we propose a machine learning based method to predict the gas price of next blocks combined with a dynamic feature exploited from mempool after the proposal of EIP-1559. Specifically, we consider the pending transactions and their gas price in the mempool and take them as a machine learning feature for the first time. Due to the update brought by EIP-1559, we refine more features than the related works. We use machine learning models combined with the mempool features for prediction. Experiments conducted on the dataset manifest that our model combined with the mempool data shows good prediction performance, especially significantly improving the two indicators MAE and RMSE. Furthermore, we analyze and discuss the challenges of our scheme and the potential profound effects brought by our work.
Imran Yousaf, Yasir Riaz, John W. Goodell
No abstract is available for this record.
Rahmat Siauwijaya, Dewi Sanjung
Bitcoin has gained popularity as an investment asset because of its similarity to gold, which sparked the idea that bitcoin can be used as a hedging instrument to the fiat currency exchange rate. This paper aims to analyze bitcoin's volatility and return to gauge its feasibility as an investment asset, and hedging tool for the USD-IDR exchange rate with the GARCH and EGARCH models. With data on the daily closing price of bitcoin, gold, IDX composite index, and USD-IDR exchange rate from January 1, 2016, to December 31, 2020, the study attempts to find factors affecting bitcoin returns with the independent variables of bitcoinâs price, gold, and USD-IDR exchange rate by estimating their correlation. Following the analysis, this study shows that the volatility of USD-IDR exchange rates negatively influences bitcoin returns, making it a relatively safe investment asset. Additionally, the study found that bitcoin returns are not affected by the variables of gold price and the IDX composite index. However, we found that the USD-IDR exchange rate significantly affects bitcoin returns, while gold price and bitcoinâs price does not significantly affect bitcoin returns. Further, the analysis found that bitcoin is unsuitable for hedging due to its sensitivity to asymmetric shocks.
To Trung Thanh, LĂȘ Thanh HĂ , Nguyá» n Thá» Thanh Huyá»n, Tran Anh Ngoc
In this paper, we employ a time-varying parameter vector autoregression (TVP-VAR) in combination with an extended joint connectedness approach to study interlinkages between the cryptocurrency and Vietnamâs stock market by characterizing their connectedness starting from January 1, 2018, to December 31, 2021. We report that the COVID-19 health shocks impact the system-wide dynamic connectedness, which reaches a peak during the COVID-19 pandemic. Net total directional connectedness suggests that the cryptocurrency market significantly impacts Vietnamâs stock market, especially those with the largest market capitalization like Bitcoin and Ethereum. This market can be held accountable for Vietnamâs stock market volatility. In encountering the COVID-19 pandemic, the effect of the three cryptocurrencies reduced before 2020, around the end of 2019 and the beginning of 2020. However, from the end of 2020â2021, while cryptocurrencies continued their roles as net transmitters for Vietnamâs stock market.
Jinxin Cui, Aktham Maghyereh
Analyzing comovements and connectedness is critical for providing significant implications for crypto-portfolio risk management. However, most existing research focuses on the lower-order moment nexus (i.e. the return and volatility interactions). For the first time, this study investigates the higher-order moment comovements and risk connectedness among cryptocurrencies before and during the COVID-19 pandemic in both the time and frequency domains. We combine the realized moment measures and wavelet coherence, and the newly proposed time-varying parameter vector autoregression-based frequency connectedness approach (Chatziantoniou et al. in Integration and risk transmission in the market for crude oil a time-varying parameter frequency connectedness approach. Technical report, University of Pretoria, Department of Economics, 2021) using intraday high-frequency data. The empirical results demonstrate that the comovement of realized volatility between BTC and other cryptocurrencies is stronger than that of the realized skewness, realized kurtosis, and signed jump variation. The comovements among cryptocurrencies are both time-dependent and frequency-dependent. Besides the volatility spillovers, the risk spillovers of high-order moments and jumps are also significant, although their magnitudes vary with moments, making them moment-dependent as well and are lower than volatility connectedness. Frequency connectedness demonstrates that the risk connectedness is mainly transmitted in the short term (1-7 days). Furthermore, the total dynamic connectedness of all realized moments is time-varying and has been significantly affected by the outbreak of the COVID-19 pandemic. Several practical implications are drawn for crypto investors, portfolio managers, regulators, and policymakers in optimizing their investment and risk management tactics.
Îikolaos Kyriazis, Stephanos Papadamou, Panayiotis Tzeremes, Shaen Corbet
This research investigates the effects of several measures of Twitter-based sentiment on cryptocurrencies during the COVID-19 pandemic. Innovative economic, as well as market uncertainty measures based on Tweets, along the lines of Baker et al. (2021), are employed in an attempt to measure how investor sentiment influences the returns and volatility of major cryptocurrencies, developing on non-linear Granger causality tests. Evidence suggests that Twitter-derived sentiment mainly influences Litecoin, Ethereum, Cardano and Ethereum Classic when considering mean estimates. Moreover, uncertainty measures non-linearly influence each cryptocurrency examined, at all quantiles except for Cardano at lower quantiles, and both Ripple and Stellar at both lower and higher quantiles. Cryptocurrencies with lower values are found to be unaffected by investor sentiment at extreme values, however, prove to be profitable due to more aligned investor behaviour.
Saliha Theiri, Ramzi Nekhili, Jahangir Sultan
Purpose This study examine the response of liquidity of Bitcoin and Ethereum to the Russia-Ukraine war in an event study context and investigate whether the war had a transitory or a permanent effect on cryptocurrency liquidity. Design/methodology/approach A event study was applied to hourly transactions on Bitcoin and Ethereum cryptocurrencies from 1/02/2022 to 31/03/2022. This is period is subdivided in two sample periods to capture transitory and permanent effects. The transitory effect is investigated over a window spanning -20 and +20 days. For a more extended post-event period, a linear regression model was applied to analyze the effects of other factors on the liquidity risk of BTC and ETH. Findings The findings reveal a significant but temporary impact of the RussiaâUkraine war on the liquidity of Bitcoin and Ethereum. Liquidity levels have increased within the first two days around the event day and then returned to the pre-event level after that. However, the response of BTC and ETH cryptocurrencies' liquidities to the Russian invasion of Ukraine is not uniform. Originality/value This is the first paper that assesses the liquidity level of two major cryptocurrencies (Bitcoin and Ethereum) in response to an extreme event: the RussiaâUkraine war. The hypothesis is that trading in the cryptocurrency market will increase due to market participants' goal of evading regulatory sanctions. Furthermore, market participants may also take advantage of cryptocurrencies' popularity as safe-haven assets.