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2,329 results · page 66 of 98
Matthias Scharnowski, Stefan Scharnowski, Stefan Scharnowski, Lukas Zimmermann
No abstract is available for this record.
Ilja Kantorovitch, Janko Heineken
No abstract is available for this record.
Haneol Cho, Kyu‐Hwan Lee, Chansoo Kim
No abstract is available for this record.
Michael C. Burda
This paper surveys the capacity of simple macroeconomic models - 'three easy pieces' - to account for persistent and positive valuations of privately issued assets based on the blockchain. Each of these three models - transactions demand for a means of payment, consumption-based capital asset pricing, and search and matching - highlights important aspects of digital payments. The mutual interference of these jointly produced features may impede widespread use of cryptocurrencies until technological innovations have been developed to separate them.
Yunchuan Sun, Xiangyi Kong, Tongrui Chen, Hang Su · 6 authors
Compared with stock market, cryptocurrency market is more susceptible to investor sentiment at the lack of substantial asset support. This study develops a proxy to measure the investor sentiment of cryptocurrency market by using textual analytics on millions of posts in Chain Node, which is the most active online community for Chinese cryptocurrency investors. We investigate the correlation between the sentiment and the market return from Jan. 2018 to Aug. 2020. The study argues that the proposed proxy could well reflect the investor sentiment of the cryptocurrency.
Dejan Živkov, Slavica Manić, Jasmina Đurašković, Dejan Viduka
This study aims to determine which auxiliary asset – S&P500, SHCOMP, the U.S. 10Y bond, gold, Brent or corn, in combination with Bitcoin has the best downside risk-minimizing performances. Six portfolios are constructed via an optimal DCC-GARCH model, while for downside risk measures, we use parametric and semiparametric Value-at-Risk and Conditional Value-at-Risk. All selected auxiliary assets have very low dynamic correlation with Bitcoin, which classifies them as good diversifiers. According to parametric results, S&P500 has the best downside risk-minimizing output, while SHCOMP and gold take second and third place. However, when higher moments of portfolios are taken into account, the results change significantly. Due to very high kurtosis and negative skewness, portfolio with S&P500 has among the worst semiparametric downside risk results. On the other hand, SHCOMP index and gold have relatively favourable third and fourth moments’ characteristics, which pushes them to the first and second place of the best auxiliary assets when modified downside risk measures are at stake. We also calculate Sharpe ratio, which suggests that portfolio with gold has by far the best return/risk characteristics.
Linus Wilson
We look at the association between the price of a cryptocurrency and the secondary market prices of the hardware used to mine it. We find the prices of the most efficient Graphical Processing Units (GPUs) for Ethereum mining are significantly positively correlated with the daily price returns to that cryptocurrency.
Raja Nabeel‐Ud‐Din Jalal, Simona Leonelli
No abstract is available for this record.
Ingolf Gunnar Anton Pernice, Anna Almosova, Hermann Elendner
No abstract is available for this record.
Mahdi Ghaemi Asl, Elie Bouri, Sahar Darehshiri, David Gabauer
No abstract is available for this record.
Simon Trimborn, Yang Li
No abstract is available for this record.
Victoria Dobrynskaya, Mikhail Dubrovskiy
The authors consider a variety of cryptocurrency and equity risk factors as potential forces that drive cryptocurrency returns and carry risk premiums. In a cross-section of 2,000 biggest cryptocurrencies during 2014–2020, only downside market risk, cryptocurrency size and cryptocurrency policy uncertainty factors are systematically priced with significant premiums. Cryptocurrencies, which have greater exposures to these factors, yield higher returns subsequently. Equity market risk, particularly equity downside market risk, appears to be more important than cryptocurrency market risk, suggesting greater linkages between cryptocurrency and equity markets than we used to think. Global and the US equity factors are more relevant for the cryptocurrency market than local factors from other markets. However, there is no evidence that exposure to momentum, volatility and Fama–French factors is compensated by higher returns.
N. Passalis, Solon Seficha, Avraam Tsantekidis, Anastasios Tefas
No abstract is available for this record.
Jianqin Hang, Xu Zhang
This study proposes a novel approach that incorporates rolling‐window estimation and a quantile causality test. Using this approach, Google Trends and Bitcoin price data are used to empirically investigate the time‐varying quantile causality between investor attention and Bitcoin returns. The results show that the parameters of the causality tests are unstable during the sample period. The results also show strong evidence of quantile‐ and time‐varying causality between investor attention and Bitcoin returns. Specifically, our results show that causality appears only in high volatility periods within the time domain, and causality presents various patterns across quantiles within the quantile domain.
Carol Alexander, Jun Deng, Bin Zou
We consider the hedging problem where a futures position can be automatically liquidated by the exchange without notice. We derive a semi-closed form for an optimal hedging strategy with dual objectives - to minimise both the variance of the hedged portfolio and the probability of liquidations due to insufficient collateral. The optimal solution depends on the statistical characteristics of the spot and futures extreme returns and parameters that characterise the hedger by loss aversion, choice of leverage and collateral management. An empirical analysis of bitcoin shows that the optimal strategy combines superior hedge effectiveness with a reduction in the probability of liquidation. We compare the performance of seven major direct and inverse hedging instruments traded on five different exchanges, based on minute-level data. We also link this performance to novel speculative trading metrics, which differ markedly between venues.
Cathy Yi‐Hsuan Chen, Dmitri Vinogradov
No abstract is available for this record.
Zhiyong Cheng, Jun Deng, Tianyi Wang, Mei Yu
Using the generalized extreme value theory to characterize tail distributions, we address liquidation, leverage and optimal margins for bitcoin long and short futures positions. The empirical analysis of perpetual bitcoin futures on BitMEX shows that (1) daily forced liquidations to outstanding futures are substantial at 3.51% and 1.89% for long and short; (2) investors got forced liquidation do trade aggressively with average leverage of 60X; and (3) exchanges should elevate current 1% margin requirement to 33% (3X leverage) for long and 20% (5X leverage) for short to reduce the daily margin call probability to 1%. Our results further suggest that normality assumption on return significantly underestimates optimal margins. Policy implications are also discussed.
Gopinath Ramkumar
No abstract is available for this record.
Vasilios Plakandaras, Elie Bouri, Rangan Gupta
Previous studies have provided evidence that trade-related uncertainty tends to predict an increase in Bitcoin returns. In this paper, we extend the related literature by examining whether the information on the US–China trade war can be used to forecast the future path of Bitcoin returns, controlling for various explanatory variables. We apply ordinary least square (OLS) regression, support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO) techniques that stem from the field of machine learning, and we find weak evidence of the role of the trade war in forecasting Bitcoin returns. Given that out-of-sample tests are more reliable than in-sample tests, our results tend to suggest that future Bitcoin returns are unaffected by trade-related uncertainties, and investors can use Bitcoin as a safe haven in this context.
Chuanhai Zhang, Huan Ma, Gideon Bruce Arkorful, Zhe Peng
No abstract is available for this record.
Jawad Saleemi
The cryptocurrency market is emerging as a new asset class for the investment. As the traditional asset prices are often noted to be influenced by the liquidity risk, this study links the cryptocurrency liquidity cost to its yields. Pre-pandemic uncertainty, the Bitcoin liquidity cost was found to be priced in its returns during the same trading session. Post-pandemic crisis, the relationship was changed. The liquidity cost was reported not to be priced in the Bitcoin returns at the time of same trading session. Post-pandemic crisis, however, the liquidity cost imposed by the liquidity supplier on day t − 1 was noted to be priced in the Bitcoin return of day t . In the cryptocurrency market, this study quantifies the effects on the Bitcoin returns of its liquidity cost, and if such effects vary pre- and post-pandemic uncertainty.
Bo Tang, Yang You
Cryptocurrency prices differ across countries, and these price deviations fluctuate widely. Our paper provides evidence that distrust toward domestic authorities can explain the dynamics of local cryptocurrency prices relative to the U.S. dollar price. The price deviation rises after an outbreak of a financial crisis, political scandal, or socioeconomic event that undermines confidence in the domestic government or economy. With panel regressions, we show that Bitcoin price deviations increase by 1.8% when the institutional failure index rises by one standard deviation. These price responses are much stronger in countries with lower trust levels and during periods with tighter capital controls.
Jiarui Zhang
The cryptocurrency market recently gained a lot of attention from investors. But, its volatility has been acting as a disincentive to investment. Volatility plays an important role in shaping market riskiness and investment behavior. We study the volatility of the Ethereum (ETH) cryptocurrency from the following perspectives. The first goal of this study is to identify risk-seeking behavior in the ETH cryptocurrency market. We examine this propensity by measuring the effect of the volatility of Ethereum on the total ETH assets. This investigation also takes the form of a case-study of an unexpected ETH fund-stolen event, DAO Hack, and the hard fork treatment. We also forecast a downward volatility trend in the near future based on Autoregressive models. This is the first study to analyze DAO Hack with empirical methods and marks the starting point for more rigorous models to predict the volatility of Ethereum.
Kwok Ping Tsang, Zichao Yang
No abstract is available for this record.