This paper examines the behaviour of Bitcoin returns and those of several other cryptocurrencies in the pre and post period of the introduction of the Bitcoin futures market. We use the principal component-guided sparse regression (PC-LASSO) model to analyze several sample sizes for the pre and post periods. Besides the neighbourhood of the break time, the current period is also investigated as returns start to recover after some time. Search intensity is observed to be the most important variable for Bitcoin for all periods, whereas for the other cryptocurrencies there are other variables that seem more important in the pre period, while search intensity still stands out in the post period. Furthermore, GARCH analyses suggest that search intensity increases the volatility of Bitcoin returns more in the post period than it does in the pre period. Our empirical findings suggest that the top five cryptocurrencies are substitutes before the launch of Bitcoin futures. However, this effect is lost, and moreover, there are spillover effects on altcoins during both the post and the recovery period. We find a spillover effect of the introduction of bitcoin futures on altcoins and this effect seems to persist during the recovery period.
Purpose Our study focuses on analyzing the trading behaviour of the investors who invest in these currencies to review their trading patterns which may help us to understand the price formation of cryptocurrencies in this market. Design/methodology/approach We used Chang et al. (2000) measure to calculate herding that is based on cross-section absolute dispersion of stock returns (CSAD). We further analyse the nature of the same in different market regimes, that is up market, down market, high volatile market, low volatile market etc. Findings Applying different methodologies both static and time varying, we find that herding is pronounced when the market is either passing through stress or has become highly volatile. Anti-herding is found in a less volatile market or in a bullish market. Practical implications Our results are also helpful for the policy makers in designing stricter regulations to provide safe investment environment to the investors. Originality/value Our study in an extension of the literature in same direction and contribute in numerous ways. As the number of digital currencies is growing day by day and we have around 2,200 digital currencies trading across the world, we increased our sample size up to 100 most traded currencies. While majority of the studies cover the period 2015–2018, our study comprises the largest sample size starting from August 2013 to April 2019. We use the static model to find herding and simultaneously try to detect herding under different market regimes: up market and down market.
Abstract Crypto currencies have sparked great interest lately not only among regular people, billionaires and Wall Street, but it also caught the attention of national and global financial regulators across the world. In this article, we try to answer the following questions: what is bitcoin? It is money, a mean of payment, a huge bubble or just a way to evade taxes, launder money and fund illegal trade? We will answer these questions by testing whether bitcoin is a bubble with the help of right-tailed ADF tests and analyzing if the price of bitcoin has experienced shocks. We identify bitcoin price shock when the price of bitcoin is above its Hodrick-Prescott trend plus one standard deviation. Also, we will analyze if bitcoin fulfils the roles of money and if itself or a stablecoin like Libra can attain an important place within the international monetary system. We will also research the potential risks associated with the adoption of Libra, especially in poorer countries. Despite Bitcoin and Libra’s weaknesses, an advantage is that they insist on the necessity of faster and cheaper cross-border funds transfers 24/7, 365 days a year.
Twitter is becoming an increasingly popular platform used by financial analysts to monitor and forecast financial markets. In this paper we investigate the impact of the sentiments expressed in Twitter on the subsequent market movement, specifically the bitcoin exchange rate. This study is divided into two phases, the first phase is sentiment analysis, and the second phase is correlation and regression. We analyzed tweets associated with the Bitcoin in order to determine if the user’s sentiment contained within those tweets reflects the exchange rate of the currency. The sentiment of users over a 2-month period is classified as having a positive or negative sentiment of the digital currency using the proposed CNN-LSTM deep learning model. By applying Pearson's correlation, we found that the sentiment of the day (d) had a positive effect on the future Bitcoin returns on the next day (d+1). The prediction accuracy of the linear regression model for the next day's revenue was 78%.
Pınar Kaya Soylu, Mustafa Okur, Özgür Çatıkkaş, Ayca Altintig
This paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long memory property using, Rescaled Range Statistics (R/S), Gaussian Semi Parametric (GSP) and the Geweke and Porter-Hudak (GPH) Model Method. Our findings show that squared returns of three cryptocurrencies have a significant long memory, supporting the use of fractional Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) extensions as suitable modelling technique. Our findings indicate that the Hyperbolic GARCH (HYGARCH) model appears to be the best fitted model for Bitcoin. On the other hand, the Fractional Integrated GARCH (FIGARCH) model with skewed student distribution produces better estimations for Ethereum. Finally, FIGARCH model with student distribution appears to give a good fit for Ripple return. Based on Kupieck’s tests for Value at Risk (VaR) back-testing and expected shortfalls we can conclude that our models perform correctly in most of the cases for both the negative and positive returns.
Ricardo de Souza Tavares, João F. Caldeira, Gerson de Souza Raimundo Júnior
This paper examines the formation prices in the cryptocurrency market using the CAPM model based on OLS and Regime-Switching approaches. Following Baek & Elbeck’s argument that internal factors drove cryptocurrency returns, CAPM was built, taking the CRIX index as the market and ten cryptocurrencies as assets. The results suggest that the market risk factor can partially explain cryptocurrency returns. Moreover, the regime change estimation positively impacts the market risk determination power for cryptocurrencies.
In contrast with robust systems that resist noise or fragile systems that break with noise, antifragility is defined as a property of complex systems that benefit from noise or disorder. Here we define and test a simple measure of antifragility for complex dynamical systems. In this work we use our antifragility measure to analyze real data from return prices in the stock and cryptocurrency markets. Our definition of antifragility is the product of the return price and a perturbation. We explore different types of perturbations that typically arise from within the system. Our results suggest that for both the stock market and the cryptocurrency market, the tendency among the 'top performers' is to be robust rather than antifragile. It would be important to explore other possible definitions of antifragility to understand its role in financial markets and in complex dynamical systems in general.
Multifractal processes reproduce some of the stylised features observed in financial time series, namely heavy tails found in asset returns distributions, and long-memory found in volatility. Multifractal scaling cannot be assumed, it should be established; however, this is not a straightforward task, particularly in the presence of heavy tails. We develop an empirical hypothesis test to identify whether a time series is likely to exhibit multifractal scaling in the presence of heavy tails. The test is constructed by comparing estimated scaling functions of financial time series to simulated scaling functions of both an iid Student t-distributed process and a Brownian Motion in Multifractal Time (BMMT), a multifractal processes constructed in Mandelbrot et al. (1997). Concavity measures of the respective scaling functions are estimated, and it is observed that the concavity measures form different distributions which allow us to construct a hypothesis test. We apply this method to test for multifractal scaling across several financial time series including Bitcoin. We observe that multifractal scaling cannot be ruled out for Bitcoin or the Nasdaq Composite Index, both technology driven assets.
María de la O González, Francisco Jareño, Frank S. Skinner
This article examines the connectedness between Bitcoin returns and returns of ten additional cryptocurrencies for several frequencies—daily, weekly, and monthly—over the period January 2015–March 2020 using a nonlinear autoregressive distributed lag (NARDL) approach. We find important and positive interdependencies among cryptocurrencies and significant long-run relationships among most of them. In addition, non-Bitcoin cryptocurrency returns seem to react in the same way to positive and negative changes in Bitcoin returns, obtaining strong evidence of asymmetry in the short run. Finally, our results show high persistence in the impact of both positive and negative changes in Bitcoin returns on most of the other cryptocurrency returns. Thus, our model explains about 50% of the other cryptocurrency returns with changes in Bitcoin returns.
Cryptocurrencies represent a new type of digital asset that cannot be linked to the framework of fundamental and systematic factors of existing financial instruments of the traditional capital market. Due to the lack of strictly defined fundamental indicators, supported by the results of research by the academic community, considering cryptocurrencies as investment opportunities can put investors in a subordinate position, a situation of complete uncertainty. Cryptocurrencies and their entire technical infrastructure are still a kind of unknown to the general public. Due to this, but also the lack of a regulatory framework, investors have to rely on sometimes uncertain information gathered through various media platforms. However, regardless of the type of assets and the mentioned shortcomings, when constructing a portfolio, investors should consider the dynamics of returns of potential components of the portfolio in order to identify and quantify the assumed investment risk and define the expected return. Cryptocurrencies are based on the idea of decentralization initially introduced by bitcoin blockchain technology and as such have their own historical sequence of origin. Since bitcoin is the first digital currency based on asymmetric cryptography, the change in its value can serve as a leading indicator of the movement of the cryptocurrency market as a whole. Accordingly, this paper will formally identify and describe the performance of the cryptocurrency portfolio with different optimization goals taking into account the assumption of a significant systematic impact of bitcoin cryptocurrency on the dynamics of the value of the aggregate secondary cryptocurrency market. For this purpose, six optimization targets will be formed: MinVar, MinCVaR, MaxSR, MaxSTARR, MaxUT and MaxMean. The results of the formed portfolios will be compared with the results of portfolios with the same allocation objectives, but which include a limitation on the impact of BTC as a systematic factor. The results suggest that by controlling the exposure by factor, better overall portfolio performance can be achieved through higher returns and Sharpe Ratio in four of the six implemented optimization strategies, while in terms of absolute risk measure five out of six portfolios achieved lower overall risk. Also, the obtained results confirm that the bitcoin transaction system plays a major role in defining the future movement of the value of the secondary cryptocurrency market.
Heshan Sameera Kankanam Pathiranage, Huilin Xiao, Weifeng Li
Cryptocurrency is an emerging phenomenon set to revolutionize the financial industry. It utilizes blockchain technology, which ensures data is immutable, transparent and reliable. Cryptocurrency is present in different forms like Blockchain and is present in most parts of the world. Developed nations have a significant interest in technology and have put a measure to investigate while developing nations are also following suit. The global interest in cryptocurrency is associated with the fact that the movement of money will be traceable and it will minimize activities such as money laundering and corruption. The paper analyzes a selected period of six years to establish the efficiency of the Blockchain in the markets. A graph and tables are essential in generating more data for interpretation of the events. The tests used in the analysis are the Ljung-box, R/S Hurst, BDS, Runs, AVR test, and Bartels tests. All the tests demonstrated inefficiencies in the Bitcoin trend, especially in the full sample and slightly low inefficiency in the later subsample. The conclusion made is that bitcoin will improve its efficiency over time as it is an emerging industry that needs to grow. Developing countries experience higher inefficiency combined with the prevalent policy challenges and issues like corruption and unemployment.
<title>Abstract</title> <bold>Background: </bold>Cryptocurrencies, especially Bitcoin, has become popular for investors in recent years. The volatility of bitcoin and time horizon are the center point for investment decisions. However, attention is not often drawn to the relationship between bitcoin and equity indices. This study investigates the volatility and time frequency domain of bitcoin among five Asean countries through a rich database which covers daily data from July 2010 until April 2019.<bold>Methods: </bold>Advanced econometrics and Wavelets Cross-Coherence Spectrograms, this study investigates the existence of long run association between bitcoin and the studied market indices. M-GARCH analysis is been employed to investigate the unconditional volatility of market indices and Bitcoin.<bold>Results: </bold>The findings present the long run association<bold> </bold>with positive (Philippines) and negative (Japan, Korea, Singapore, Hong Kong) relations. Moreover, only one market (KOREA) shows a short run association with bitcoin. The M-GARCH analysis reveals, most of the selected Asean countries have a low unconditional volatility with bitcoin. Except for Philippines in which the co-movement is average, Wavelet analysis reveals the presence of a strong and long co-movements for most of the selected Asean countries with bitcoin.<bold>Conclusions: </bold>Most of our results are consistent and illustrate different dimensions of long and short run relationship, volatilities, correlations, and time-frequency analysis. This study utilized Asean emerging economies which are rarely available in the literature as existing studies are more skewed towards the West. We believe the outcomes of this study will be a significant for industry practitioners (i.e., retail and institutional investors) on designing better strategies to diversify the stock portfolio with different holding period horizons and dimensions.
Purpose This paper aims to elaborate on the optimization of two particular cryptocurrency portfolios in a mean-variance framework. In general, cryptocurrencies can be classified to as coins and tokens where the first can be thought of as a medium of exchange and the latter accounts for security or utility tokens depending upon its design. Design/methodology/approach Against this backdrop, this empirical study distinguishes, in particular, between pure coin and token portfolios. Both portfolios are optimized by maximizing the Sharpe ratio and, subsequently, compared with alternative portfolio strategies. Findings The empirical findings demonstrate that the maximum utility portfolio of coins, with a risk aversion of λ = 10, outweighs alternative frameworks. The portfolios optimized by maximizing the Sharpe ratio for both coins and tokens indicate a rather poor performance. Testing the maximized utility for different levels of risk aversion confirms the findings of this empirical study and confers them more robustness. Research limitations/implications Further investigation is strongly recommended as tokens represent a new phenomenon in the cryptocurrency universe, for which only a limited amount of data are available, which restricts the sampling. Furthermore, future study is to include more sophisticated optimization models using different constraints in portfolio creation. Practical implications In light of the persistently substantial volatility in cryptocurrency markets, the empirical findings assert that portfolio managers are advised to construct a global minimum variance portfolio. In the absence of sophisticated optimization models, private investors can invest according to the market values of cryptocurrencies. Despite minor differences in the risk and reward ratios of the portfolios tested, tokens tend to be more speculative, especially, if the Tether token is excluded, which may require enhanced supervision and investor protection by regulating authorities. Originality/value As the current literature investigates on diversification effects of blended cryptocurrency portfolios rather than making an explicit distinction, this paper reflects one of the first to explore the investability and role of diversifying coins and tokens using a classic Markowitz approach.
Huaigang Long, Adam Zaremba, Ender Demir, Jan Jakub Szczygielski · 5 authors
This study presents the first attempt to examine the cross-sectional seasonality anomaly in cryptocurrency markets. To this end, we apply sorts and cross-sectional regressions to investigate daily returns on 151 cryptocurrencies for the years 2016 to 2019. We find a significant seasonal pattern: average past same-weekday returns positively predict future performance in the cross-section. Cryptocurrencies with high same-day returns in the past outperform cryptocurrencies with a low same-day return. This effect is not subsumed by other established return predictors such as momentum, size, beta, idiosyncratic risk, or liquidity.