This paper is saddled with the task of investigating the Bitcoin market behaviour in the presence of a government risk. This is because both the institutional and retail investors' interests in the Bitcoin market is growing rapidly. Conversely, the seemingly unregulated nature of this market is a serious concern to most economies and results to the placement of ban on Initial Coin Offering (ICO) in some economies by the government. Daily series of return and volume within the window of the ICO ban in China was used for the Bitcoin market and S&P500 stock market to examine the effect of a government risk in the Bitcoin market and possible hedging capabilities. Empirical results show that the ban dampened Bitcoin returns and the returns from each market can predict the other. The Exogenous Dynamic Conditional Correlation (Exo-DCC) model result suggests that, yes! the S&P500 stocks is capable of hedging Bitcoin risk while Bitcoin can also hedge S&P500 stocks risks and vice versa. The Exogenous BEKK (Exo-BEKK) model result shows evidence of bidirectional volatility spill over between the two markets studied. In practice, investors (institutions and retailers) can comfortably form a robust investment portfolio with (at least) these two assets and develop a hedging strategy such that the impacts of risks on the portfolio's returns are safely hedged.
The purpose of this study is to explore the market maturity of cryptocurrency trading platforms based on the information transmission perspective of financial market price volatility. This study uses the volatility spillovers index proposed by Diebold and Yilmaz [1][2] and uses the bitcoin trading platform to measure the total price volatility of cryptocurrency trading platforms, and the directional spillovers among the trading platforms. The sample period is from January 1, 2015 to September 30, 2018. In the empirical process, each sub-sample is taken every three months. The argument of this research indicated that if the cryptocurrency trading platforms' total spillover effects, with the rolling of the sub-sample period, show the increasing trend, and the trading platform has a staggered spillover effect with each other, indicating that cryptocurrency trading platforms exist the chaotic phenomenon, and the cryptocurrency market is in a stage of low maturity. On the contrary, if the total spillover effects are showing a decreasing trend and the spillover effects are mainly from a certain minority trading platforms, indicating that the cryptocurrency trading platforms present the order phenomenon, and cryptocurrency market is at a stage of high maturity. The contribution of this research is to identify the market maturity of the cryptocurrency trading platform, and to promote policy makers to propose a market-building mechanism for the market situation, so that the cryptocurrency has the opportunity to become a mainstream trading tool.
Κωνσταντίνος Γκίλλας, Elie Bouri, Rangan Gupta, David Roubaud
In this paper, we extend existing studies by considering the relationships across crude oil, gold, and Bitcoin markets. Using high-frequency data from December 2, 2014 to June 10, 2018, we analyze spillovers in volatility jumps and realized second, third, and fourth moments across crude oil, gold, and Bitcoin markets via Granger causality and generalized impulse response analyses in daily frequency. Results suggest evidence of predictability and emphasize, among others, the need of jointly modeling linkages across those three markets with higher-order moments; otherwise, inaccurate risk assessment and investment inferences may arise. The responses of realized volatility shocks and volatility jump are generally positive. Furthermore, results indicate evidence of a weaker relationship between gold – crude oil, and Bitcoin – crude oil compared to the case of Bitcoin - gold. Practical implications are discussed.
Within the decision-making process, investors are interested in finding the most effective solutions that will allow them to obtain short-term benefits. Current economic environment is characterized by the emergence of new financial instruments that can assist investors to diversify their investment portfolio. Crypto-currencies represents a category of financial assets that can be used by investors to reduce risk and achieve significant returns. Therefore, the study intends to analyze the financial behavior of investors in the moment of publishing the financial statements. Financial statements could have a positive or negative influence on the investment portfolio and structure. The issue analyzed by this study is represented by the ability of the cryptocurrency Bitcoin to be considered as an alternative investment asset. The study is divided into two parts. In the first part, the study presents the review of literature about value-relevance, cryptocurrency term and speculative bubble. The second part presents the research methodology and results. The results of the study validate the hypothesis of this study, cryptocurrency Bitcoin being a financial asset that can be used as an alternative investment asset for diversification of investment portfolio.
Abstract This paper provides a comprehensive overview of cryptocurrencies, including the origin of cryptocurrencies, how cryptocurrencies operate, and the current situation of cryptocurrencies. In addition, we also provide the performance comparison of major cryptocurrencies with the performance of the stock market indexes. All the cryptocurrencies exhibit higher average returns and volatility than the stock market indexes, which appeals to risk-taking investors. We then perform additional analysis on the determinants of cryptocurrencies returns. We show that major fundamental variables are less likely to affect the returns of cryptocurrencies except for the S&P 500 index returns and the exchange rates between U.S. dollars and Euros.
This study examines the volatility of certain cryptocurrencies and how they are influenced by the three highest capitalization digital currencies, namely the Bitcoin, the Ethereum and the Ripple. We use daily data for the period 1 January 2018-16 September 2018, which represents the bearish market of cryptocurrencies. The impact of the decline of these three cryptocurrencies on the returns of the other virtual currencies is examined with models of the ARCH and GARCH family, as well as the DCC-GARCH. The main conclusion of the study is that the majority of cryptocurrencies are complementary with Bitcoin, Ethereum and Ripple and that no hedging abilities exist among principal digital currencies in distressed times.
<h3>Practical Applications Summary</h3> In <b>Beyond Bitcoin: <i>A Statistical Comparison of Leading Cryptocurrencies and Fiat Currencies and Their Impact on Portfolio Diversification</i></b> from the Summer 2019 issue of <b><i>The Journal of Alternative Investments</i></b>, authors <b>Stefan Ehlers</b> and <b>Kolja Gauer</b> (both at <b>Volkswagen AG</b>) provide a first-of-its-kind analysis of whether traditional currencies (also known as fiat currencies) and cryptocurrencies act similarly or differently with respect to their fluctuations in value and total return. The authors also explore whether mixing cryptocurrencies and fiat currencies in an investment portfolio can help diversify it and reduce the portfolio’s variance. The authors find no correlation between the fluctuations in value and total return of cryptocurrencies and fiat currencies, so combining them in a mixed portfolio improves diversification. Also, only Bitcoin and XRP play an important role in reducing the variance of a pure cryptocurrency portfolio, while just a few cryptocurrencies and fiat currencies significantly reduce the variance of mixed portfolios. So, those who want to invest in cryptocurrencies and avoid major swings in value and returns should consider including a few specific currencies in their portfolio and should combine cryptocurrencies with fiat currencies in a mixed portfolio. <b>TOPICS:</b>Currency, statistical methods, portfolio construction
This paper examines the impact of South Korea’s ban on Bitcoin futures on intraday spot volatility, liquidity and volatility–volume relationship. The results show that while reducing the permanent component of intraday spot volatility, the imposition of a ban on Bitcoin futures trading increases the transitory component. For intraday spot liquidity, different liquidity proxies indicate heterogeneous results. Moreover, we identify a positive and unidirectional effect of intraday spot volume on volatility. This effect appears to be stronger in the post-ban period. Overall, over the past few months, South Korea’s Bitcoin futures ban generally has had a significant impact on the intraday dynamics of the Bitcoin spot market.
Abstract Bitcoins are traded on various exchange platforms and, therefore, prices may differ across trading venues. We aim to investigate return connectedness across eight of the major exchanges of Bitcoin, both from a static and a dynamic viewpoint. To this end, we employ an extension of the order‐invariant forecast error variance decomposition proposed by Diebold and Yilmaz (2012) to a generalized vector error correction framework. Our results suggest that there is strong connectedness among the exchanges, as expected, although some of them behave dissimilarly. We identify Bitfinex and Coinbase as leading exchanges during the considered period, while Kraken as a follower exchange. We also obtain that connectedness across exchanges is strongly dynamic, as it evolves over time.
How do cryptocurrency prices evolve? Is there any interdependence among cryptocurrency returns and/or volatilities? Are there any return spillovers and volatility spillovers between the cryptocurrency market and other financial markets? To answer these questions, we use GARCH-in-mean models to examine the relationship between volatility and returns of leading cryptocurrencies, to investigate spillovers within the cryptocurrency market, and also from the cryptocurrency market to other financial markets. Overall, we find statistically significant transmission of shocks and volatilities among the leading cryptocurrencies. We also find statistically significant spillover effects from the cryptocurrency market to other financial markets in the United States, as well as in other leading economies (Germany, the United Kingdom, and Japan).
Purpose The purpose of this paper is to examine the day-of-the-week effects of Bitcoin (BTC) markets on the exchange level from January 2014 to September 2018. Design/methodology/approach The in-depth study on the day-of-the-week effects is conducted by using data consisting of Bitcoin prices denominated in 20 fiat currencies from 23 Bitcoin trading exchanges through the method of rolling sample for calendar effect proposed by Zhang et al. (2017). Findings It is shown by the empirical results that different patterns of the day-of-the-week effects are observed on Bitcoin denominated in various fiat currencies by referring to the price data collected from exchanges. Furthermore, the patterns of the day-of-the-week effects are also available after adjusting Bitcoin prices denominated in domestic currencies into USD. Research limitations/implications Because of the discontinuity of data for some daily return series, estimation with dynamic variance is not applicable. It is assumed that the error item follows normal distribution with constant variance. Originality/value The day-of-the-week effects are wide-spread in Bitcoin markets, and they are not mainly caused by movements of foreign exchange rates. Actually, empirical findings in this study provide evidence for inefficiency of Bitcoin markets.
Purpose The purpose of this paper is to shed fresh light into whether an energy commodity price index (ENFX) and energy blockchain-based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market. Design/methodology/approach Using principal component analysis over daily data of crude oil, heating oil, natural gas and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices. Findings Natural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only lagged ENCX was significant in estimating current ENFX. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX. Weak forecasting results of the VAR model, support the two markets are not robust forecasters of each other. Robustness wise, the VAR model ranked lower than an autoregressive model, but higher than a random walk model. Research limitations/implications Significant structural breaks at distinct dates in the two markets reinforce that the two markets do not help to predict each other. The findings are limited by the existence of bubbles (December 2017-January 2018) which were witnessed in energy blockchain-based crypto markets and natural gas, but not in crude oil and heating oil. Originality/value As per the authors’ knowledge, this is the first paper to analyze the relationship between leading energy commodities and energy blockchain-based crypto markets.
We study the time varying co-movement patterns of the crypto-currency prices with the help of wavelet-based methods; employing daily bilateral exchange rate of four major crypto-currencies namely Bitcoin, Ethereum, Lite and Dashcoin. First, we identify Bitcoin as potential market leader using Wavelet multiple correlation and Cross correlation. Further, Wavelet Local Multiple Correlation for the given crypto-currency prices are estimated across different time-scales. From the results, it is found that that the correlation follows an aperiodic cyclical nature, and the crypto-currency prices are driven by Bitcoin price movements. Based on the results obtained, we suggest that constructing a portfolio based on crypto-currencies may be risky at this point of time as the other crypto-currency prices are mainly driven by Bitcoin prices, and any shocks in the latter is immediately transformed to the former.
We examine the predictive power of economic policy uncertainty, volume, transaction activity, and Twitter on Bitcoin between 27 December 2013 and 11 February 2019 using the recently proposed time-varying Granger causality tests of Shi et al. (2018). First, of particular interest, we show that volume can only predict Bitcoin returns during two episodes (August 2016-January 2017 and May 2017-June 2017) based on a Wald test with a recursive evolving procedure under a homoskedasticity error assumption. However, volume cannot predict volatility under any specifications. Secondly, both US economic policy uncertainty and equity market uncertainty indices, which are used as proxies for policy uncertainty, have no effect on predicting Bitcoin returns. Thirdly, transaction activity also cannot predict Bitcoin returns. Lastly, the number of tweets about Bitcoin can Granger cause the volume of Bitcoin (for example, March 2015-August 2015 and January 2016-February 2019) but not returns or volatility.