Abstract This paper investigates whether market quality, uncertainty, investor sentiment and attention, and macroeconomic news affect bitcoin price discovery in spot and futures markets. Over the period December 2017–March 2019, we find significant time variation in the contribution to price discovery of the two markets. Increases in price discovery are mainly driven by relative trading costs and volume, and uncertainty to a lesser extent. Additionally, medium‐sized trades contain most information in terms of price discovery. Finally, higher news‐based bitcoin sentiment increases the informational role of the futures market, while attention and macroeconomic news have no impact on price discovery.
Francisco López Herrera, División de Investigación, Facultad de Contaduría y Administración, Universidad Nacional Autónoma de México, Ciudad de México, México., Luis Guadalupe Macías-Trejo, Oscar V. De la Torre-Torres
Este artículo muestra los resultados de un análisis del desempeño de ocho de los criptoactivos más importantes entre la gran variedad que actualmente existe en el mercado. Se estudia su riesgo de mercado con base en métricas ampliamente utilizadas para activos financieros. El análisis se complementa con la evaluación de su desempeño dentro de portafolios formados con criterios convencionales. Se encuentra un comportamiento bastante heterogéneo entre los activos estudiados, sugiriendo que tal comportamiento obedece a las características específicas de cada uno de ellos, más que a las características comunes como una clase específica de activos.
In this article, we empirically examine the cryptocurrency market reaction to china prohibiting initial coin offerings, on the 4 September 2007 for the 100 largest cryptocurrencies. The announcement has a significant negative but temporary impact on cryptocurrency returns and liquidity.
Yuanyuan Zhang, Stephen Chan, Jeffrey Chu, Hana Sulieman
The market for cryptocurrencies has experienced extremely turbulent conditions in recent times, and we can clearly identify strong bull and bear market phenomena over the past year. In this paper, we utilise algorithms for detecting turnings points to identify both bull and bear phases in high-frequency markets for the three largest cryptocurrencies of Bitcoin, Ethereum, and Litecoin. We also examine the market efficiency and liquidity of the selected cryptocurrencies during these periods using high-frequency data. Our findings show that the hourly returns of the three cryptocurrencies during a bull market indicate market efficiency when using the detrended-fluctuation-analysis (DFA) method to analyse the Hurst exponent with a rolling window. However, when conditions turn and there is a bear-market period, we see signs of a more inefficient market. Furthermore, our results indicated differences between the cryptocurrencies in terms of their liquidity during the two market states. Moving from a bull to a bear market, Ethereum and Litecoin appear to become more illiquid, as opposed to Bitcoin, which appears to become more liquid. The motivation to study the high-frequency cryptocurrency market came from the increasing availability of higher-frequency cryptocurrency-pricing data. However, it also comes from a movement towards higher-frequency trading of cryptocurrency. In addition, the efficiency of cryptocurrency markets relates not only to whether prices are predictable and arbitrage opportunities exist, but, more widely, to topics such as testing the profitability of trading strategies and determining the maturity of cryptocurrency markets.
Purpose The purpose of this study is to test whether the price traders are prepared to pay for Bitcoin, over the market price, is related to a country’s level of corruption and lack of economic freedom, during Bitcoin’s most turbulent period, 2017-2018. Design/methodology/approach Bitcoin premiums (the excess over the market price) are calculated for 17 countries from April 2017 to September 2018, using daily weighted Bitcoin prices compared to the market price and testing against the Corruption Perception Index, the Index of Economic Freedom and changes in the Economic Uncertainty Index. Findings On testing Bitcoin premiums across 17 countries, it is found that the price paid for Bitcoin above the market price goes hand-in-hand with the level of corruption and decline in economic freedom. As corruption increases so does the premium paid for Bitcoin, and as the level of economic freedom declines, the premium increases. This relationship holds during very different market conditions: the increasing Bitcoin price from April to September 2017; the rise and fall in Bitcoin prices from October 2017 to March 2018; the declining market price from April to September 2018; and in December 2017 when Bitcoin reached over US$19,000. Originality/value Rather than focussing on daily returns which measure changes from day to day, this research uses all transactions during the day focussing on an intra-day consensus price measured in local currency. With such large price increases, percentage measures can hide the size of the monetary response. By focussing on the monetary impact, the differences between countries become more apparent.
Previous studies demonstrate the existence of recurrent arbitrage opportunities in the cryptocurrency market and describe strategies that can be used to profit from them. There is, however, limited research describing the practical aspects and challenges of adopting such strategies. This paper covers the design, implementation, and evaluation of the high-frequency intramarket arbitrage strategy on the leading cryptocurrency exchange Binance. It also describes the notion of arbitrage, cryptocurrency market, and the approaches to implementing the automated trading system. Methods for assets selection, arbitrage detection algorithms, optimization, and real-time testing are discussed. The research findings confirm the existence of the expected market inefficiencies leading to the arbitrage opportunities and the profitability of the implemented system under certain trading conditions.
This study proposes a method to enhance cryptocurrency portfolios constructed by forecast models. This study forecasts returns on four liquid cryptocurrencies (Bitcoin, Litecoin, Ripple, and Dash) and determines the weights on the cryptocurrencies based upon a dynamic allocation framework. We assess the performances of the portfolios using the performance fee measure. Our results present that the proposed portfolios outperform the benchmark portfolio with the conventional level of the risk aversion parameter. The economic gain for an investor is equivalent to 12% per week. The economic gain is sensitive to a change in the risk aversion parameter, which contrasts with the studies of exchange rates which is due to the high volatility on the cryptocurrencies. Our predictors are related to the price momentum effects and they outperform widely used network factors.
The purpose of this thesis is to study the predictability of cryptocurrency returns by investor attention, the interconnections of the cryptocurrency market, and what causes attention to cryptocurrencies. This is done by examining Bitcoin, Ethereum and Ripple which are the three biggest cryptocurrencies by market capitalization in January 2020. The dataset is constructed from weekly returns, weekly changes in investor attention measured by Google trend data and weekly changes in average weekly trading volume between years 2016 and 2019. The empirical analysis is conducted by performing OLS regressions, vector autoregressions and Granger causality tests. Additional robust tests are conducted by dividing the sample in pre-bubble and post-bubble samples adding all of the investor attention proxies to individual Cryptocurrency regressions. The results suggest that the market phase for a cryptocurrency affects the predictability of returns as the statistically significant positive relationship between investor attention disappears in the post-bubble sample for Bitcoin and Ethereum but endures for Ripple in both samples. This provides more evidence for the earlier findings that cryptocurrencies become more efficient as the market matures. The interconnections of the cryptocurrency market are shown to exist as the returns of Bitcoin drive investor attention to Ripple which is shown to be a significant predictor for all of the three cryptocurrencies in the whole sample. The spillover effect is shown to take time confirming earlier findings and unfolding the herding effect via investor attention in cryptocurrencies. Additionally, investor attention is shown to be caused by earlier returns for the cryptocurrency as well as the returns of Bitcoin. These results explain the interconnections of cryptocurrencies, the changing market dynamics in the cryptocurrency market, and the predictability of cryptocurrency returns by investor attention.
The aim of the paper is to check if cryptocurrency Bitcoin – a new investable asset class representative – is able to improve the performance of an optimal portfolio. Using two Markowitz criteria of optimization – expected return maximization and expected shortfall (CVaR) minimization – we test the investment opportunities after adding Bitcoin to the portfolio of 10 traditional assets (among them equity, fixed income, money, commodities and money market indices). Using daily observations from 1.05.2013 till 24.05.2019, we examine the behavior of the portfolios without and with Bitcoin and check if the return-risk ratio improves for the latter. Discussing the results, we conduct the sensitivity analysis by changing the lookback window (LB) and rebalancing frequency (RB) parameters. Empirical analysis suggests that Bitcoin-inclusive portfolios provide an investor with wider diversification opportunities. Robustness check confirms the findings and also advocates for the cryptocurrency to be added to the portfolio.
This paper investigates the relations between multiple measures of investor sentiment and the returns, volatility, trading volume, and liquidity. Using both data outside and inside market, we find that the Bullishness from socio-finance model are significant related to future realized volatility and trading volume, similar to Tweet, which is thought to capture information of well-informed investors in Bitcoin market
This paper analyzes the stability of stablecoins and proposes a framework to test for absolute and relative stability of stablecoins. Based on high-frequency data, we find strong evidence of excess price variations. While Bitcoin is a likely source of this excess volatility because stablecoin returns, volatility and volumes are highly correlated with corresponding Bitcoin time-series, we also demonstrate through a quasi-natural experiment that stablecoins increase the trading volume of Bitcoin. The findings suggest stablecoins play a key role in cryptocurrency markets.
The prices of cryptocurrencies are very volatile and forecasting them is a challenging task for the researchers across the world. The present study examines the accuracy of forecasted returns of the two most popular cryptocurrencies (Bitcoin and Ethereum) for the sample period spanning from October 1, 2013, to November 30, 2018. Auto-regressive integrated moving average (ARIMA) and Neural Network models have been used to forecast the returns of the cryptocurrencies. The forecasting results for different time-horizons indicate that for a shorter time-horizon, ARIMA model is better for forecasting the returns of cryptocurrencies, whereas, for a longer time-horizon, Neural Network model is better for forecasting the returns of cryptocurrencies. These results have implications for traders, investors, regulators, policymakers and academia.
The study measures the risk level linked with different portfolios of cryptocurrencies by using portfolio diversification techniques. Data on prices and trade volumes of cryptocurrencies were collected on a daily basis, from 2012 till 2018. Ten portfolios were constructed based on diverse types and numbers of cryptocurrencies. The results of the study confirm a negative relationship between the average number of cryptocurrencies and the average risk level of the portfolio. Involving more cryptocurrencies within the portfolio reduces the diversification risk of the portfolio. The average volatility and average correlation coefficient both drop when moving towards portfolios with more cryptocurrencies. Average returns stand against portfolio theories, where more risky portfolios offer less daily weighted average returns and the other way around. Outcomes of the study provide indications for the individual investors and financial institutions on the risk characteristic attached to the portfolio of cryptocurrencies.
The outbreak of Wuhan pneumonia in China in January 2020, we observed that sharp falls in Chinese stock markets were often followed by a brief drop in the price of Bitcoin followed by a notable increase. The fact that the outbreak of infectious disease in China had little impact on US markets and at least a portion of the funds flowed back to the US through Bitcoin transactions suggests that the price of Bitcoin is related to an outflow of Chinese funds to the US. Our analysis combining the computational aspects of cumulative prospect theory with the stochastic dominance method indicates that investors facing instability on Chinese markets use Bitcoin for hedge trading, perhaps as an intermediary in times of emergency.