Purpose With a substantial return and volatility characteristic of Bitcoin, which may be seen as a new category of investment assets, better understanding of the nature of return and volatility spillover can help investors and regulators in achieving the potential goal from portfolio diversification. The paper aims to discuss these issues. Design/methodology/approach This paper explores the return and volatility transmission between the Bitcoin, as the largest cryptocurrency, and other traditional asset classes, namely stock, bond and currencies from the standpoint of Turkey over the period July, 2010–June, 2018 using the newly developed multivariate econometric technique, VAR–GARCH, in mean framework with the BEKK representation. Findings The empirical results reveal the existence of the positive unilateral return spillovers from the bond market to Bitcoin market. Regarding the results of shock and volatility spillovers, there exists strong evidence of bidirectional cross-market shock and volatility spillover effects between Bitcoin and all other financial asset classes, except US Dollar exchange rate. Originality/value The important extention is the adoption of a newly developed multivariate econometric technique, VAR–GARCH, in mean framework with the BEKK representation, proposed by Engle and Kroner (1995), which is employed for the first time specifically to examine the extent of integration in terms of volatility and return between Bitcoin and key asset classes. Second, Bitcoin has experienced a rapid growth since around a decade and a number of investors are showing interest in its potential as an integrative part of portfolio diversification. The information provided by empirical results gives empirical bases from which to address topics concerning hedging purposes and optimal portfolio allocation. It is also increasingly important to analyze the current behavior of Bitcoin in relation to other assets to provide policy makers and regulatory bodies with guidance on the role of the Bitcoin as an investment asset in Turkey. Thus, this is the first serious attempt at exploring the potential for Bitcoin to offer diversification opportunities in the context of Turkey.
The rapid development of cryptocurrencies has drawn attention to this particular market, with investors trying to understand its behaviour and researchers trying to explain it. The evolution of cryptocurrencies’ prices showed a kind of bubble and a crash at the end of 2017. Based on this event, and on the fact that Bitcoin is the most recognized cryptocurrency, we propose to evaluate the contagion effect between Bitcoin and other major cryptocurrencies. Using the Detrended Cross-Correlation Analysis correlation coefficient (ΔρDCCA) and comparing the period after and before the crash, we found evidence of a contagion effect, with this particular market being more integrated now than in the past—something that should be taken into account by current and potential investors.
V. Dimitrova, M. Fernández–Martínez, M.A. Sánchez-Granero, Juan Evangelista Trinidad Segovia
In this paper, we explore the (in)efficiency of the continuum Bitcoin-USD market in the period ranging from mid 2010 to early 2019. To deal with, we dynamically analyse the evolution of the self-similarity exponent of Bitcoin-USD daily returns via accurate FD4 approach by a 512 day sliding window with overlapping data. Further, we define the memory indicator by the difference between the self-similarity exponent of Bitcoin-USD series and the self-similarity index of its shuffled series. We also carry out additional analyses via FD4 approach by sliding windows of sizes equal to 64, 128, 256, and 1024 days, and also via FD algorithm for values of q equal to 1 and 2 (and sliding windows equal to 512 days). Moreover, we explored the evolution of the self-similarity exponent of actual S&P500 series via FD4 algorithm by sliding windows of sizes equal to 256 and 512 days. In all the cases, the obtained results were found to be similar to our first analysis. We conclude that the self-similarity exponent of the BTC-USD (resp., S&P500) series stands above 0.5. However, this is not due to the presence of significant memory in the series but to its underlying distribution. In fact, it holds that the self-similarity exponent of BTC-USD (resp., S&P500) series is similar or lower than the self-similarity index of a random series with the same distribution. As such, several periods with significant antipersistent memory in BTC-USD (resp., S&P500) series are distinguished.
Purpose This paper aims to empirically investigate the volatility of Bitcoin, Litecoin and the Euro. Design/methodology/approach The authors use quantitative methodologies to assess the annualized volatility of two cryptocurrencies and one international fiat currency. The exchange rate of the currencies is monitored on a daily basis using 1,460 observations from January 1, 2014 to December 31, 2017. The models used include the augmented Dickey–Fuller test, Akaike Information Criteria, autocorrelation function and exchange rate changes determining which currency is the most volatile. Findings The findings indicate, based on the statistical measures used, including the standard deviation of selected currencies and annualized volatility, that Litecoin is more volatile than Bitcoin and the Euro and that Bitcoin is more volatile than the Euro. This furthers previous research on cryptocurrency volatility. Originality/value The paper provides compelling evidence about the volatility of Litecoin and Bitcoin. The volatility of cryptocurrencies is furthered with data that are more current. The findings are important for investors, financial markets and central banks.
Sanal ve kripto para niteliğinde olan Bitcoin, dijital formata sahip, teknik olarak blok zinciri olarak ifade edilen işlemleri kapsayan ve merkezi para sistemine dahil olmayan bir para birimidir. Çalışmada, kripto para Bitcoin ile döviz kurları arasındaki ilişkiyi ortaya çıkarmak amaçlanmıştır. ABD Doları bazında Bitcoin kuru ile Euro, Japon Yeni, İngiliz Sterlini, Avustralya Doları, Kanada Doları, İsviçre Frankı, Yuan Renminbisi ve İsveç Kronu döviz kurları arasındaki ilişki, 3.02.2012-04.10.2017 dönemindeki günlük kur değerleri esas alınarak, yapısal kırılmalı Gregory ve Hansen eşbütünleşme ve Granger nedensellik analizleri ile incelenmiştir. Analiz sonucunda, BTC/USD döviz kurunda yapısal kırılmaların, 2013 yılı Nisan ve Aralık aylarında gerçekleştiği belirlenmiştir. Ayrıca çalışmada, döviz kurlarına ilişkin zaman serileri arasında uzun dönemli eşbütünleşme ilişkisi tespit edilirken, CNY/USD döviz kuru ile BTC/USD döviz kuru arasında tek yönlü pozitif nedensellik ilişkisi tespit edilmiştir.
We compare the ability of two measures of uncertainty, a newspaper-based measure and an internet search-based measure, to predict Bitcoin returns. Using monthly data from July 2010 to May 2019 and a predictive regression model characterized by a heteroskedastic error structure and, we show that Bitcoin is a hedge against both measures. However, the predictive content of the internet-derived uncertainty related queries measure is statistically stronger than the measure of uncertainty based on newspapers for predicting Bitcoin returns, which is possibly due to the fact that the measure of uncertainty is now directly obtained from individual investors via internet searches.
Bitcoin is an online communication protocol, which facilitates electronic transactions. It has grabbed the attention of investors and researchers in the recent past. The non-regulatory feature of Bitcoin makes it riskier and the element of speculation in its trading is higher than any other financial asset. The study provides an insight into the price dynamics of Bitcoin by examining the day of the week and month of the year effect for the period 2013 to 2017. The findings of the study indicate the existence of seasonality in the return behaviour of Bitcoin as returns for Monday is higher than any other day of the week. Likewise, the returns earned during the month of November are significantly different from other months of the year. The results of the study assert a violation of the assumption of weak-form market efficiency and imply that the Bitcoin market provides an opportunity for the investors to exploit the market from its predictable behavior and fetch abnormal gains.
Asset pricing models and investment styles have been researched intensively in equities, bonds, FX, and commodities. However, a new asset class has emerged since the end of 2008, namely, cryptocurrencies such as Bitcoin and Ethereum, among others. The author uses an extensive data set of over 1,500 cryptocurrencies and shows that almost none of the traditional investment styles such as momentum or defensive appear to be successful in this young asset class. Cryptocurrencies are also independent from the macroeconomic environment and cannot be explained by a standard asset pricing model. A cryptocurrency specific model yields clearly better results. In addition, the whole cryptocurrency space is dominated by only a few individual digital coins. Equally weighted mean monthly returns appear to be random with low or even no correlation with traditional asset classes such as US equities and global FX. <b>TOPICS:</b>Currency, portfolio construction, risk management, performance measurement
En este artículo se estima la distribución multivariada para analizar la dependencia del Bitcoin (BTC), Ripple (XRP) y Ether (ETH). Se utiliza la familia Hiperbólica Generalizada de distribuciones (GH) y en particular la distribución Varianza Gamma. El procedimiento para la estimación de los parámetros de la GH es a través del algoritmo EM (Expectation-Maximization). Los resultados muestran que existe una dependencia positiva entre los tres tipos de cambio respecto del dólar americano y se estima una distribución Varianza-Gamma de dimensión tres. Esta distribución es muy flexible para el ajuste de series de los rendimientos con leptocurtosis y sesgo. Esta información se considera importante para los inversionistas que conforman sus portafolios de una manera eficiente.
Anwar Hasan Abdullah Othman, Syed Musa Alhabshi, Razali Haron, Azman Bin Mohd. Noor
Bank stability and trust levels have been seriously affected by the 2007–2008 Global Financial Crisis, which led to the identification of a range of policies based on past experience intended to overcome the consequences of the crisis. The idea of cryptocurrency was introduced to handle the mistrust of financial intermediaries that led to the liquidity crisis. This study investigates whether the new cryptocurrencies have been able to perform the functions of financial intermediaries and offer the confidence level required by bank depositors, examining their long-run effect on banks’ deposit mobilization. The study applies a cointegration test analysis with a vector error correction model to examine this relationship. Overall results indicate that in the countries studied increases in the market capitalization of the cryptocurrency industry have a significant negative impact on banks’ deposit variability, while decreases have a positive impact, with a long-run equilibrium relationship. The outcomes of the study suggest that in order to regain the trust of depositors and avoid the effects of the cryptocurrency industry, banks should be encouraged to invest directly in cryptocurrency or should consider cryptocurrencies as an alternative investment asset for their portfolio investment diversification strategies during bullish market conditions and avoid them during bearish market conditions. Also, banks can incorporate blockchain technology into their operation system and compete with the cryptocurrency industry side by side in the financial market. If neither of these options is taken, however, the banking system may not be able to compete and sustain in the long term using the current operational model. The outcomes of this study can be used as policy guidance by central banks and the banking industry for the betterment of the industry. <b>TOPICS:</b>Currency, legal/regulatory/public policy, statistical methods, portfolio construction
The Bitcoin market becomes the focus of the economic market since its birth, and it has attracted wide attention from both academia and industry. Due to the absence of regulations in the Bitcoin market, it may be easier to bring some kinds of illegal behaviors. Thus, it raises an interesting question: Is there abnormity or illegal behavior in Bitcoin platforms? To answer this question, we investigate the abnormity in five leading Bitcoin platforms. By analyzing the financial index, i.e. the normalized logarithmic price return, we find that the properties of price return in bitFlyer are completely different from others. To find the possible reasons, we find that the abnormal ask price and bid price appear simultaneously in bitFlyer, which may be potentially linked to either price manipulation or money laundering. It verifies our conjecture that there may be abnormity or price manipulation in Bitcoin platforms. Furthermore, our findings in price return could also provide an innovative and effective method to detect the abnormity in Bitcoin platforms.
Ziaul Haque Munim, Mohammad Hassan Shakil, Ilan Alon
This study analyzes forecasts of Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and without re-estimation of the forecast model for each step. For cross-validation of forecast results, we consider two different training and test samples. In the first training-sample, NNAR performs better than ARIMA, while ARIMA outperforms NNAR in the second training-sample. Additionally, ARIMA with model re-estimation at each step outperforms NNAR in the two test-sample forecast periods. The Diebold Mariano test confirms the superiority of forecast results of ARIMA model over NNAR in the test-sample periods. Forecast performance of ARIMA models with and without re-estimation are identical for the estimated test-sample periods. Despite the sophistication of NNAR, this paper demonstrates ARIMA enduring power of volatile Bitcoin price prediction.
Abstract\nThis paper applies cointegration tests to identify cryptocurrency pairs which can be used in pairs trading strategies. The aim of this research is twofold. First, I want to examine cointegration in a system of bitcoin, dashcoin, dogecoin and litecoin. In the second part, I create pairs trading strategies in order to determine whether excess return can be made, compared to a simple buy and hold approach. The results find evidence of cointegration between the cryptocurrencies and positive profitability using pairs trading. By creating a portfolio in which the funds are equally allocated to the strategies with an open position, excess return can be made.
Franco Valencia, Alfonso Gómez-Espinosa, Benjamín Valdés-Aguirre
Cryptocurrencies are becoming increasingly relevant in the financial world and can be considered as an emerging market. The low barrier of entry and high data availability of the cryptocurrency market makes it an excellent subject of study, from which it is possible to derive insights into the behavior of markets through the application of sentiment analysis and machine learning techniques for the challenging task of stock market prediction. While there have been some previous studies, most of them have focused exclusively on the behavior of Bitcoin. In this paper, we propose the usage of common machine learning tools and available social media data for predicting the price movement of the Bitcoin, Ethereum, Ripple and Litecoin cryptocurrency market movements. We compare the utilization of neural networks (NN), support vector machines (SVM) and random forest (RF) while using elements from Twitter and market data as input features. The results show that it is possible to predict cryptocurrency markets using machine learning and sentiment analysis, where Twitter data by itself could be used to predict certain cryptocurrencies and that NN outperform the other models.
Imtiaz Sifat, Azhar Mohamad, Mohammad Syazwan Bin Mohamed Shariff
This paper investigates lead-lag relationship between heavyweight cryptocurrencies Bitcoin and Ethereum. Traditional studies of information flow between markets preponderate on cash vs. futures, whereby researchers are interested in the stabilizing impact of futures on spot markets. While interest in the same relationship in the nascent cryptocurrency sphere is emerging, little is known regarding price leadership between these assets. In this paper, we employ a battery of statistical tests—VECM, Granger Causality , ARMA, ARDL and Wavelet Coherence—to identify price leadership between the two crypto heavyweights Bitcoin and Ethereum. Based on one year hourly and daily data from August 2017 through to September 2018, our tests yield varied results but largely suggest bi-directional causality between the two assets. Moreover, the results indicate that intraday crypto traders can barely exploit Bitcoin-Ethereum hourly or daily price discovery process to their advantage.