A new market has emerged from active trading of major cryptocurrencies and the listing of many initial coin offering (ICO) tokens. The authors conduct an early investigation into potential factor structures in the expected returns of crypto assets. They find that crypto assets with large market capitalization, low volatility, and high past returns tend to outperform in the following month. These are suggestive evidences for an emerging factor structure, even though crypto asset returns are still largely dominated by idiosyncratic noise. Their findings could help investors make better decisions in the nascent crypto asset market. <b>TOPICS:</b>Currency, exchanges/markets/clearinghouses, quantitative methods
This article considers the problem of testing for an explosive bubble in financial data in the presence of time-varying volatility. We propose a sign-based variant of the Phillips, Shi, and Yu (2015, International Economic Review 56, 1043–1077) test. Unlike the original test, the sign-based test does not require bootstrap-type methods to control size in the presence of time-varying volatility. Under a locally explosive alternative, the sign-based test delivers higher power than the original test for many time-varying volatility and bubble specifications. However, since the original test can still outperform the sign-based one for some specifications, we also propose a union of rejections procedure that combines the original and sign-based tests, employing a wild bootstrap to control size. This is shown to capture most of the power available from the better performing of the two tests. We also show how a sign-based statistic can be used to date the bubble start and end points. An empirical illustration using Bitcoin price data is provided.
David Vidal-Tomás, Ana M. Ibáñez, José Emilio Farinós Viñas
Cryptocurrencies have attracted the attention of many investors and policymakers given the increase in popularity of Bitcoin. In this context, we analyse the cryptocurrency market by means of cap-weighted and equally weighted market portfolios that include all the altcoins available for three different periods (2015–2017, 2016–2017 and 2017). By using the most traditional tests of efficiency, we observe three main features of the cryptocurrency market: it is weak-form inefficient due to the behaviour of all the altcoins, it is more inefficient over time, especially in 2017, and the creation of new cryptocurrencies has not significantly changed the efficiency of the market.
We examine and compare a large number of generalized autoregressive conditional heteroskedastic (GARCH) and stochastic volatility (SV) models using series of Bitcoin and Litecoin price returns to assess the model fit for dynamics of these cryptocurrency price returns series. The various models examined include the standard GARCH(1,1) and SV with an AR(1) log-volatility process, as well as more flexible models with jumps, volatility in mean, leverage effects, t-distributed and moving average innovations. We report that the best model for Bitcoin is SV-t while it is GARCH-t for Litecoin. Overall, the t-class of models performs better than other classes for both cryptocurrencies. For Bitcoin, the SV models consistently outperform the GARCH models and the same holds true for Litecoin in most cases. Finally, the comparison of GARCH models with GARCH-GJR models reveals that the leverage effect is not significant for cryptocurrencies, suggesting that these do not behave like stock prices.
This paper investigates the importance of "time of execution" and the relevance of "precision time" in order driven transactions done over distributed ledgers. We created a distributed marketplace using stock market price data from the Toronto Stock Exchange (TMX). We then proceeded to test and measure the impact of timing of orders at the nanosecond level. Whilst price discovery in order driven markets is done instantaneously, with distributed markets, it is necessary to know which order to process first to avoid "front-running". We argue that a protocol for the time of order of receipt and execution should be subject to nanosecond stacking. Our approach incorporates both transitory and permanent price discovery components. It allows for the efficient processing of transactions and the order that are received by a market clearing distributed ledger.
Elie Bouri, Rangan Gupta, Chi Keung Marco Lau, David Roubaud
We study whether level of risk aversion can be used to predict Bitcoin returns. Using a copula-quantile approach, we find evidence of predictability for the lower and upper quantiles of the conditional distribution of returns (i.e., in bull and bear markets). To reveal the sign of the predictability, we apply the cross-quantilogram approach and find that the cross-quantilogram is similar when risk aversion is at the low or medium level for various quantiles of Bitcoin returns. In particular, we find positive predictability when the risk aversion is very low and at the medium level. However, the predictability becomes negative when both the risk aversion and Bitcoin returns are very high, suggesting that very high levels of risk aversion are likely to drive down Bitcoin returns in a bull market.
This paper fits in the trend of discussing the efficiency of cryptocurrency markets. Since 2008, when Bitcoin appeared on the market, arbitrageurs from all over the world have been trying to find the gaps in the markets, which will let them earn risk-free money using financial operations. Although a lot of researchers are trying to figure out arbitrage opportunities, looking at different exchanges and using different cryptocurrencies, so far hardly anyone has looked at arbitrage opportunities with the use of FIAT currencies within the same or different exchanges. This paper examines such opportunities for three different exchanges, i.e. Kraken, Bitfinex and Bitstamp -exchanges that enable trading in USD and EUR against Bitcoin at the same time. The main empirical results suggest that there are significant arbitrage opportunities on these markets. In the paper, we also show the main constraints in FIAT currencies arbitrage on cryptocurrency exchanges.
This letter investigates the dynamic relationship between market efficiency, liquidity, and multifractality of Bitcoin. We find that before 2013 liquidity is low and the Hurst exponent is less than 0.5, indicating that the Bitcoin time series is anti-persistent. After 2013, as liquidity increased, the Hurst exponent rose to approximately 0.5, improving market efficiency. For several periods, however, the Hurst exponent was found to be significantly less than 0.5, making the time series anti-persistent during those periods. We also investigate the multifractal degree of the Bitcoin time series using the generalized Hurst exponent and find that the multifractal degree is related to market efficiency in a non-linear manner.
Abstract We investigate whether Chinese cryptocurrency investors show confirmatory bias when processing authority‐related news. Authority‐related news is defined as news that is related to government authority (including central bank) policies or talk. By using data from the largest cryptocurrency exchange in China, we find that investors’ response to authority‐related news is negative and significant in general. Moreover, we find that the abnormal trading volume and standard deviation of abnormal trading volume are significantly higher for authority‐related news with higher readability, suggesting investors respond to the more readable authority‐related news with more trading behaviour.
Matthias Schnaubelt, Jonas Rende, Christopher Krauß
The majority of electronic markets worldwide employ limit order books, and the recently emerging exchanges for cryptocurrencies pose no exception. With this work, we empirically analyze whether commonly observed empirical properties from established limit order exchanges transfer to the cryptocurrency domain. Based on the literature, we establish a structured methodological framework to conduct analyses in a systematic and comprehensive way. We then present results from a unique and extensive limit order data set acquired from major cryptocurrency exchanges for the currency pair Bitcoin to US Dollar. We recover many observations from mature markets, such as a symmetry between the average ask and the average bid side of the order book, autocorrelation in returns on the smallest time scales only, volatility clustering and the timing of large trades. We also observe some idiosyncrasies: The distributions of trade size and limit order prices deviate from commonly observed patterns. Also, we find limit order books to be relatively shallow and liquidity costs to be relatively high when compared to established markets.
In this study, we investigate the relationship between Bitcoin mining technology variables and Bitcoin returns, using a GARCH-M model. Additionally, we examine the predictive power of the mining technology variables on future Bitcoin returns. We find that mining difficulty and block size are inversely related to Bitcoin returns. Additionally, our findings signifying that the higher the block size the lower the Bitcoin price and consequently the lower the expected return. Second, our findings show that mining difficulty and block size are robust predictors of future Bitcoin returns.
A peer-to-peer network for conducting encrypted digital trade called cryptocurrency was created eight years ago. The first and most well-known cryptocurrency, Bitcoin, is leading the charge as a disruptive technology to decades-old, largely unaltered financial payment infrastructure. Although cryptocurrencies are unlikely to displace traditional fiat money, they might alter how Internet-connected global markets communicate with one another by removing restrictions imposed by conventional national currencies and exchange rates. Technology develops quickly, and the success of a particular technology is almost entirely determined by the market it attempts to better.
Bitcoin has emerged to become the most popular cryptocurrency and its presence\nhas the potential to disrupt existing payment and monetary systems. Over the past\ndecade, the bitcoin price has exhibited extreme volatility, puzzling for both academics\nand market practitioners. We examine the dynamic relationship between\ninvestor attention and the bitcoin price using principal component analysis and vector\nerror correction models and discover that investor attention is an important contributor\nin bitcoin price formation. Variance decomposition analysis suggests that\ninvestor attention explain a significant amount of future variations in the bitcoin\nprice, and investor attention can be used to predict direction of future price change.\nOur study offers insight into the bitcoin market and the economic impact of investor\nattention.
Zhongxue Chen, Dominic Lewinski, Guoyi Zhang, Yiming Yang
The cryptocurrency market is different from traditional markets due to its unique property, which allows global trading around the clock. It is of interest to investigate if some traditional stock market phenomena still exist in the cryptocurrency markets. In this research, we studied the application of the 75% reversion rule in cryptocurrency markets. Using local linear regression, we identified active markets at certain time and location, and examined government regulations and news' influence on the cryptocurrency markets.