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1.
This paper aims to investigate the safe-haven properties of gold and two cryptocurrencies, Bitcoin and Ether. Safe havens are the financial assets that allow investors to protect their portfolios within the market turmoil. The research sample covers five years and includes several downturns on the financial markets, starting from the Chinese stock market turbulences in 2015/2016 and ending up with the recent pandemic outbreak in 2020. We find that only gold used to be a strong safe-haven against the stock market indices. Yet, this property evaporated during the crisis caused by the COVID pandemic. Occasionally, cryptocurrencies could have been considered weak safe-havens against the examined instruments. Ether acted more often as a weak safe-haven against DAX or S&P500, while Bitcoin played this role against FTSE250, STOXX600 and S&P500. 相似文献
2.
There is a growing stream of empirical research that endeavors to identify the influential variables contributing to the price formation of cryptocurrencies and, in particular, Bitcoin. However, results of those studies generally remain inconsistent in terms of not only the true combination of factors that affect Bitcoin prices, but also the nature of effects (positive vs. negative) that each individual factor has on the price behavior. The present study investigates the robustness of a wide variety of candidate determinants that have been the focus of attention in relevant literature. Our inquiry relies on the extreme bounds analysis (EBA), which is a type of large-scale sensitivity analysis capable of addressing model uncertainty issues. The findings suggest that crypto market forces of supply and demand, public interest, and economic policy uncertainty are the only variables robust to all possible variations in the conditioning information set. Our evidence argues in favor of the predominance of cryptocurrency-related determinants over global macroeconomic and financial ones in explaining Bitcoin price movements. 相似文献
3.
As blockchain platforms are becoming increasingly noticeable in financial services and beyond, questions arise regarding their suitability to compete with or replace existing payment systems and marketplaces and redesign the financial infrastructures of the future. Prominent among these concerns are issues around governance and control in distributed ledgers: How are distributed ledger technologies governed? Can blockchains address complex administration problems? What key issues of note for practitioners and academics have emerged thus far? In this paper we aim to review the existing governance practices of established or popular blockchain and decentralized autonomous organization (DAO) systems with a view to understanding how they hold up in times of crises. What questions are raised when they are compromised or faced consensus challenges in coordinating action especially around control and accountability? We use a translational process, generating focal insights about present concerns from the reference point of completed academic studies and extensive practitioner consultation. Rather than adopting a declarative approach attempting to provide all the answers, we draw insights from the IT platform governance literature to offer a critical perspective for asking the right questions around key governance issues in financial infrastructure such as decision rights, control mechanisms, and incentives. 相似文献
4.
As the first kind of digital cryptocurrency, the Bitcoin price cycle provides an opportunity to test bubble theory in the digital currency era. Based on the existing asset bubble theory, we verified the Bitcoin bubble based on the production cost with the application of VAR and LPPL models, and this method achieved good predictive power. The following conclusions are reached: (1) PECR is constructed to depict the deviation degree between the price and production cost, while BC is used to illustrate the bubble size in the price, and both are effective measures; (2) the number of unique addresses is a suitable measure of the use value of Bitcoin, and this result has passed the Granger causality test; (3) PECR and BC are verified via the LPPL model, and the next large bubble is expected in the second half of 2020. Considering that Bitcoin will see 'output halved' in May 2020, this prediction is a high-probability event. 相似文献
5.
This paper reports evidence of intraday return predictability, consisting of both intraday momentum and reversal, in the cryptocurrency market. Using high-frequency price data on Bitcoin from March 3, 2013, to May 31, 2020, it shows that the patterns of intraday return predictability change in the presence of large intraday price jumps, FOMC announcement release, liquidity levels, and the outbreak of the COVID-19. Intraday return predictability is also found in other actively traded cryptocurrencies such as Ethereum, Litecoin, and Ripple. Further analysis shows that the timing strategy based on the intraday predictors produces higher economic value than the benchmark strategy such as the always-long or the buy-and-hold. Evidence of intraday momentum can be explained in light of the theory of late-informed investors, whereas evidence of intraday reversal, which is unique to the cryptocurrency market, can be related to investors’ overreaction to non-fundamental information and overconfidence bias. 相似文献
6.
In this paper we test for regime changes and possible regime commonalities in the price dynamics of Bitcoin, Ethereum, Litecoin and Monero, as representatives of the cryptocurrencies asset class. Several parametric models are considered for the joint dynamics of the basket price where parameters are modulated through a Hidden Markov Chain with finite state space. Best specifications within Gaussian and Autoregressive models for price differences are selected by means of the AIC and BIC information criteria and through an out-of-sample forecasting performance. The empirical results, within the period January 2016 to October 2019, suggest that three or four states may be relevant to describe the dynamics of each individual cryptocurrency, depending on the selection criteria, while the entire basket displays at most three common states. Finally, we show how the identification of appropriate models may be exploited in order to build profitable investment strategies on the considered cryptocurrencies. 相似文献
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美、欧央行监管比特币的做法及其对我国的启示 总被引：2，自引：0，他引：2
李东卫 《北京市经济管理干部学院学报》2013,(4):30-33,42
2015年12月5日,中国人民银行、银监会、证监会等五部委联合印发了《关于防范比特币风险的通知》,这对于保护社会公众的财产权益,保障人民币的法定货币地位,防范洗钱风险,维护金融稳定,具有重要的现实意义。美、欧央行对比特币的监管已先行一步,对我国具有一定的启示和借鉴。本文简要介绍了我国比特币交易情况及监管现状,归纳总结了美、欧央行监管比特币做法,提出我国应借鉴美、欧央行做法制定应急预案,防范比特币风险。 相似文献
9.
This article proposes an equilibrium approach to lottery markets in which a firm designs an optimal lottery to rank-dependent expected utility (RDU) consumers. We show that a finite number of prizes cannot be optimal, unless implausible utility and probability weighting functions are assumed. We then investigate the conditions under which a probability density function can be optimal. With standard RDU preferences, this implies a discrete probability on the ticket price, and a continuous probability on prizes afterwards. Under some preferences consistent with experimental literature, the optimal lottery follows a power-law distribution, with a plausibly extremely high degree of prize skewness. 相似文献
10.
We aim to reveal the characteristics and mechanism of the Bitcoin bubble in 2019. First, we identify the period during which two important Bitcoin bubbles occurred based on the generalized supremum augmented Dickey-Fuller (GSADF) method. There are two significant bubble cycles. The first bubble lasted approximately 26 days from November 25, 2017, to December 21, 2017, while the second bubble lasted approximately one week from June 22 to June 29, 2019. The occurrence of the first bubble was related to the considerable expansion of initial coin offerings (ICOs) in 2017, while the formation of the second bubble was affected by the release of Libra. Second, as the GSADF method cannot be used to accurately infer the time at which a bubble bursts, we employ the log-periodic power law singularity (LPPLS) model for this purpose. We verify that the LPPLS method can not only infer the timing of a bubble burst but also shows stable results. Finally, we demonstrate the implications of the 2019 bubble. During the 2019 bubble, due to the increased supervision of European and American governments and the impact of hedging assets, the bubble’s duration was shorter, and the positive feedback mechanism was not as strong as that of the 2017 bubble. In addition, the oscillating frequency of the bubble in 2019 was low and unstable, which means that it would be more beneficial for investors to hold the currency for a long time. 相似文献
11.
We consider asymmetric winner-reimbursed contests. It turns out that such contests (Sad-Loser) have multiple internal pure-strategy equilibria (where at least two players are active). We describe all equilibria and discuss their properties. In particular, we find (1) that an active player is indifferent among all her non-negative choices and her expected payoff is zero in any internal equilibrium, (2) that a higher-value (stronger) player always spends less than a lower-value (weaker) player and therefore always has a lower chance to win a Sad-Loser contest in any internal equilibrium, and (3) a sufficient condition for a net total spending to be higher in a Sad-Loser contest than in the corresponding asymmetric contest. 相似文献
12.
This study examines the dependence and contagion risk between Bitcoin (BTC), Litecoin (LTC) and Ripple (XRP) using non-parametric mixture copulas (developed by Zimmer, 2012) and recently proposed methods of full-range tail dependence copulas (advanced by Hua, 2017, Su and Hua, 2017), for the period from 04-08-2013 to 17-06-2018. The Chi-plots and Kendall plots results show heavy tail dependence between each pairs of the cryptocurrencies. Evidence from the mixture copula indicates that for the BTC-LTC pair the upper-tail dependence is both stronger and more prevalent, while for the other pairs of cryptocurrencies the lower-tail dependence is very strong and more prevalent. However, the results of the full-range tail dependence copulas reveal a strong and prevalent upper and lower-tail dependence of each pairs of cryptocurrencies. These results provide evidence of significant risk contagion among price returns of major cryptocurrencies, both in bull and bear markets. 相似文献
13.
This paper examines the relationship between investor fear in the cryptocurrency market and Bitcoin prices by considering the potential effects of the ongoing COVID-19 pandemic during the period of May 5, 2018 and December 10, 2020. The existence of structural changes in the time series for the full sample reveals a non-constant causality between fear sentiment and Bitcoin prices, which leads us to apply a bootstrap rolling window Granger causality test. Our results show that both negative and positive interactions between fear sentiment and Bitcoin prices occur during several subperiods. The nature of these interactions changes significantly before and during the pandemic. Thus, we contribute to the fast-growing literature on the financial effects of the COVID-19 global pandemic, as well as to the debate on whether to classify Bitcoin as a new asset, speculative investment, currency, or safe haven asset. 相似文献
14.
With the rapid rise of cryptocurrencies, it has become an urgent problem to realize the flat use of digital currency, with making it really put into use, and giving full play to its utility in the current economic market. This paper innovatively takes the maximization of user benefit as the key point to predict transaction bidding price combining dynamic game theory. The bidding price of user transaction not only refers to historical transactions, but also considers the impact on future subsequences, and the result describes the interaction between transactions in detail. Also this paper proposes a method to express user satisfaction and establishes a user benefit model accordingly, so as to ensure the transaction is packaged successfully to the greatest extent within the acceptable range of transaction pricing. Finally this paper compares the proposed model with conventional machine learning prediction algorithms, finding that when user does not participate in the trading for the first time, the prediction effect of this proposal is better than that of machine learning over small data sets, moreover superior to machine learning methods in prediction accuracy and sensitivity, with a lower time complexity. 相似文献
15.
Bitcoin (BTC), as the dominant cryptocurrency, has attracted tremendous attention lately due to its excessive volatility. This paper proposes the time-varying transition probability Markov-switching GARCH (TV-MSGARCH) models incorporated with BTC daily trading volume and daily Google searches singly and jointly as exogenous variables to model the volatility dynamics of BTC return series. Extensive comparisons are carried out to evaluate the modelling performances of the proposed model with the benchmark models such as GARCH, GJRGARCH, threshold GARCH, constant transition probability MSGARCH and MSGJRGARCH. Results reveal that the TV-MSGARCH models with skewed and fat-tailed distribution predominate other models for the in-sample model fitting based on Akaike information criterion and other benchmark criteria. Furthermore, it is found that the TV-MSGARCH model with BTC daily trading volume and student-t error distribution offers the best out-of-sample forecast evaluated based on the mean square error loss function using Hansen’s model confidence set. Filardo’s weighted transition probabilities are also computed and the results show the existence of time-varying effect on transition probabilities. Lastly, different levels of long and short positions of value-at-risk and the expected shortfall forecasts based on MSGARCH, MSGJRGARCH and TV-MSGARCH models are also examined. 相似文献
16.
This paper uses the quantile-on-quantile regression to examine the predictive power of transaction activity for Bitcoin returns over the period from January 2013 to December 2018. We measure the Bitcoin transaction activity using trading volumes, the number of unique Bitcoin transactions, and the number of unique Bitcoin addresses. Considering the onset of structural breaks, we identify considerable effects of the heterogeneity concerning the quantiles of transaction activity, which cannot be depicted fully by the traditional quantile regression method. The empirical results show that higher transaction activity tends to predict higher/lower Bitcoin returns when the market is in a bullish/bearish state. We find that the nexus is asymmetric across quantiles, depending on the sign and size of the transaction activity, and the predictive relationship intensifies in the upper or lower quantiles of the conditional distribution. In addition, this empirical evidence is in line with the volume-return association in the equity market due to private informative and noninformative trading actions. Overall, our findings suggest that transaction activity-based strategies should be made with respect to Bitcoin market performance, specifically during extreme conditions. 相似文献
17.
In recent years, Bitcoin exchange rate prediction has attracted the interest of researchers and investors. Some studies have used traditional statistical and econometric methods to understand the economic and technology determinants of Bitcoin, few have considered the development of predictive models using these determinants. In this study, we developed a two-stage approach for exploring whether the information hidden in economic and technology determinants can accurately predict the Bitcoin exchange rate. In the first stage, two nonlinear feature selection methods comprising an artificial neural network and random forest are used to reduce the subset of potential predictors by measuring the importance of economic and technology factors. In the second stage, the potential predictors are integrated into long short-term memory (LSTM) to predict the Bitcoin exchange rate regardless of the previous exchange rate. Our results showed that by using the economic and technology determinants, LSTM could achieve better predictive performance than the autoregressive integrated moving average, support vector regression, adaptive network fuzzy inference system, and LSTM methods, which all use the previous exchange rate. Thus, information obtained from economic and technology determinants is more important for predicting the Bitcoin exchange rate than the previous exchange rate. 相似文献
18.
In this paper, we illustrate the real function relationship between the stock returns and change of investor sentiment based on the nonparametric regression model. The empirical results show that when the change of investor sentiment is moderate, the stock return is positively correlated with the change of investor sentiment, presenting an obvious momentum effect. However, the stock return is negatively correlated with the change of investor sentiment if the change of investor sentiment is dramatic, presenting significant reversal effects. Moreover, the degree of reversal effect caused by extremely optimistic sentiment is greater than that driven by extremely pessimistic sentiment, which shows a significant asymmetry. Our findings offer a partial explanation for financial anomalies such as the mean reversion of stock returns, the characteristic of slow rise and steep fall in China's stock market and so on. 相似文献
19.
Inspired by cross-market information flows among international stock markets, we incorporate external predictive information from other cryptocurrency markets to forecast the realized volatility (RV) of Bitcoin. To make the most of such external information, we employ six widely accepted approaches to construct predictive models based on multivariate information. Our results suggest that the scaled principal component analysis (SPCA) approach steadily improves the predictive ability of the prevailing heterogeneous autoregressive (HAR) benchmark model considering both the model confidence set (MCS) test and the Diebold–Mariano (DM) test based on three widely accepted loss functions. The forecasting performance is persistent to various robustness checks and extensions. Notably, a mean–variance investor can obtain steady positive economic gains if the investment portfolio is constructed on the basis of the forecasts from the HAR-SPCA model. The results of this study show that external predictive information is statistically and economically important in forecasting Bitcoin RV. 相似文献
20.
This study aims to explain price movements in the two largest cryptocurrencies that represent the majority of cryptocurrency market capitalization—Bitcoin and Ethereum. A VAR‐GARCH‐BEKK model is estimated to analyze how Google search interest, number of tweets and active addresses on the blockchain impact prices of Bitcoin and Ethereum over time. We find solid evidence that the amount of active addresses is the most significant variable among others influencing price movements in Bitcoin and Ethereum. Based on spillover effects and GIRFs, Google searches and tweets, to a certain extent, have impacts on the Bitcoin and Ethereum prices, but the impacts are weaker than that of active addresses in terms of magnitude and significance. 相似文献