The purpose of this study was to analyse and understand the attitude of gender towards cryptocurrency. The questionnaire was formed to collect data about knowledge, experience, trust, and other investment factors of the gender towards cryptocurrency. This paper will be beneficial to the upcoming or existing companies of cryptocurrency to estimate their future viability based on gender. The research was also aimed  to the detection of gender differences within the areas of awareness, investing, mining and paying with the cryptocurrencies.  The research aimed to analyse the data collected and conclude the overall attitude of male/ female towards cryptocurrency. The findings confirmed the existence of gender differences in attitude towards crypto currencies, as the male respondents were more willing to use the crypto currencies in most of the cases.
We study whether news and sentiment about bitcoin regulation, the hacking of bitcoin exchanges and scheduled macroeconomic news announcements affect the volatility of bitcoin, measured as realized variance and its jump component. Our results show that realized variance and its jump component exhibit similar dynamics and react similarly to various types of news. Volatility of bitcoin reacts most strongly to news on bitcoin regulation, positive investor sentiment regarding bitcoin regulation extracted using Google searches, and most notably, hacking attacks on cryptocurrency exchanges. Quantile regression reveals that hacking attacks have particularly strong impact on the upper conditional distribution of bitcoin volatility. We also find that the volatility of bitcoin is not influenced by most scheduled US macroeconomic news announcements, such as government budget deficits, inflation, or even monetary policy announcements. On the other hand, bitcoin responds with increased volatility to announcements of forward-looking indicators, such as the consumer confidence index.
Purpose This paper aims to investigate the effect of the political risk on Bitcoin return and volatility during the 2016 US pre-election and post-election periods. Design/methodology/approach A daily composite political risk index is calculated by using the principal component analysis and Google Trends. A quantile regression approach is adopted to assess the effect of the political risk index on Bitcoin return and volatility for both periods subject to market conditions. Findings Findings reveal that the political risk index tends to increase when moving from the pre-election period to the post-election one. This is mostly attributed to the new challenges faced by the new elected government. During the pre-election period, the quantiles regression shows that the political risk index negatively affects Bitcoin return when the market is bearish, whereas a positive impact on volatility is found in bearish and bullish markets. When the political situation becomes severer during the post-election period, the quantiles plots show that the increase of the political risk index leads to a significant increase of Bitcoin return, whereas Bitcoin volatility remains relatively stable. This means that Bitcoin can be adopted as a hedging tool when the political situation becomes severer. Originality/value Comparing to the existed studies in the field, this paper considers Google trends as a main source to assess the daily composite political risk index during the 2016 US presidential election.
Abstract This paper evaluates the presence of regime changes in the logâreturns volatility dynamics of cryptocurrencies using MarkovâSwitching GARCH (MSâGARCH) models. The empirical study compares the prediction performance of MSâGARCH against traditional singleâregime GARCH methods for oneâ, fiveâ and tenâstepsâahead volatility forecasting of six leading digital coins such as Bitcoin, Dashcoin, Ethereum, Litecoin, Monero and Ripple. Using a Bayesian approach, different MSâGARCH structures are estimated considering specifications up to three regimes, three scedastic functions and six error distributions, resulting in a total of 54 models for each cryptocurrency. Forecasts are compared according to an economic criterion, that is, through the estimation of ValueâatâRisk (VaR) and Expected Shortfall (ES) risk measures. The results support the evidence of regime changes in the volatility process of selected cryptocurrencies and show that MSâGARCH models do provide more accurate VaR and ES forecasts than their singleâregime counterparts.
We test whether the selected cryptocurrencies exhibit long memory behavior in returns and volatility. We use data on five most traded cryptocurrencies: Bitcoin, Litecoin, Ethereum, Bitcoin Cash, and XRP. Using recent tests of long memory developed against persistent and nonlinear alternatives, this paper finds that long memory is mostly rejected in returns. The tests fail to reject the null hypothesis of long memory in most cases across different volatility proxies and cryptocurrencies. The estimated memory parameters show that volatility is persistent, and when volatility is measured by log range, it is borderline nonstationary.
The Bitcoin (BTC) market presents itself as a new unique medium currency, and it is often hailed as the âcurrency of the futureâ. Simulating the BTC market in the price discovery process presents a unique set of market mechanics. The supply of BTC is determined by the number of miners and available BTC and by scripting algorithms for blockchain hashing, while both speculators and investors determine demand. One major question then is to understand how BTC is valued and how different factors influence it. In this paper, the BTC market mechanics are broken down using vector autoregression (VAR) and Bayesian vector autoregression (BVAR) prediction models. The models proved to be very useful in simulating past BTC prices using a feature set of exogenous variables. The VAR model allows the analysis of individual factors of influence. This analysis contributes to an in-depth understanding of what drives BTC, and it can be useful to numerous stakeholders. This paperâs primary motivation is to capitalize on market movement and identify the significant price drivers, including stakeholders impacted, effects of time, as well as supply, demand, and other characteristics. The two VAR and BVAR models are compared with some state-of-the-art forecasting models over two time periods. Experimental results show that the vector-autoregression-based models achieved better performance compared to the traditional autoregression models and the Bayesian regression models.
Abstract This study investigates whether Bitcoin (BTC) can provide a hedge against the fiat currencies in Asia over various investment horizons. We focus on Asia because it is one of the fastestâgrowing regions worldwide, where cryptocurrencies are actively traded. A wavelet transform technique is combined with a multivariate factor stochastic volatility (SV) model to examine the dynamic correlation properties and risk reduction effectiveness of BTC in both the time and frequency domains. We use gold and oil as benchmarks and compared their results with those of BTC. The estimated correlations indicate that the Asian currencies tend to be negatively correlated with BTC; therefore, the latter could provide a hedge against the former over the medium (8â32 days) and long (32â64 days) terms. By contrast, Asian currencies tend to be positively correlated with oil and gold for the same horizons. We also analyze the downside risk reduction effectiveness of BTC for the portfolio of Asian currencies and find that BTC provides better risk reduction than oil and gold, particularly over the medium and long terms. This study makes significant contributions to the literature by demonstrating that the correlation properties and risk reduction effectiveness of BTC differ depending on the investment horizons. We believe our findings using the waveletâbased SV model can help heterogeneous investors detect portfolio risks and thus, identify optimal hedging strategies over various investment horizons.
Mircea Constantin Čcheau, Simona Liliana Paramon CrÄciunescu, Iulia Brici, Monica Violeta Achim
Technological development brings about economic changes that affect most citizens, both in developed and undeveloped countries. The implementation of blockchain technologies that bring cryptocurrencies into the economy and everyday life also induce risks. Authorities are continuously concerned about ensuring balance, which is, among other things, a prudent attitude. Achieving this goal sometimes requires the development of standards and regulations applicable at the national or global level. This paper attempts to dive deeper into the worldwide operations, related to cryptocurrencies, as part of a general phenomenon, and also expose some of the intersections with cybercrime. Without impeding creativity, implementing suggested proposals must comply with the rules in effect and provide sufficient flexibility for adapting and integrating them. Different segments need to align or reposition, as alteration is only allowed in a positive way. Adopting cryptocurrency decisions should be unitary, based on standard policies.
Abdelkader Derbali, Lamia Jamel, Monia Ben Ltaifa, Ahmed K. Elnagar ¡ 5 authors
Purpose This paper provides an important perspective to the predictive capacity of Fed and European Central Bank (ECB) meeting dates and production announcements for the dynamic conditional correlation (DCC) between Bitcoin and energy commodities returns and volatilities during the period from August 11, 2015 to March 31, 2018. Design/methodology/approach To assess empirically the unanticipated component of the US and ECB monetary policy, the authors pursue the Kuttner's approach and use the federal funds futures and the ECB funds futures to assess the surprise component. The authors use the approach of DCC as introduced by Engle (2002) during the period from August 11, 2015 to March 31, 2018. Findings The authorsâ results suggest strong significant DCCs between Bitcoin and energy commodity markets if monetary policy surprises are incorporated in variance. These results confirmed the financialization of Bitcoin and commodity energy markets. Finally, the DCC between Bitcoin and energy commodity markets appears to respond considerably more in the case of Fed surprises than ECB surprises. Originality/value This study is a crucial topic for policymakers and portfolio risk managers.
Muhammad Ashraf Fauzi, Norazha Paiman, Zarina OTHMAN
Bitcoin and other prominent cryptocurrencies have gained much attention since the last several years. Globally known as digital coin and virtual currency, this cryptocurrency is gained and traded within the blockchain system. The blockchain technology adopted in using the cryptocurrency has raised the eyebrows within the banking sector, government, stakeholders and individual investors. The rise of the cryptocurrency within this decade since the inception of Bitcoin in 2009 has taken the market by storm. Cryptocurrency is anticipated as the future currency that might replace the current paper currency worldwide. Even though the interest has caught the attention of users, many are not aware of its opportunities, drawbacks and challenges for the future. Researches on cryptocurrencies are still lacking and still at its infancy stage. In providing substantial guide and view to the academic field and users, this paper will discuss the opportunities in the cryptocurrency such as the security of its technology, low transaction cost and high investment return. The originality of this paper is on the discussion within law and regulation, high energy consumption, possibility of crash and bubble, and attacks on network. The future undertakings of cryptocurrency and its application will be systematically reviewed in this paper.
Zheng-Zheng Li, ChiâWei Su, Meng Qin, Muhammad Umar
This paper explores the interactions between the Bitcoin (BTC) prices in the US and Chinese markets, by employing the bootstrap rolling window causality test. The results reveal that BTC prices behave differently across markets, and also vary with time, which subjects to the theory of price discovery. In other words, the BTC price in one market could precede the other, and vice versa, based on the information advantage. Markets that are more flexible (US) respond sensitively to information, thus, in order to induce the price changes in Chinese markets. The improvements in the economic conditions of the emerging markets have exerted an influential role in global markets. Since the Chinese market possesses a considerable amount of trading volumes, the BTC price in the US can be assumed to chase the BTC price in China. The leadâlag relationship between these two markets also reflects the acknowledgement of the aversion towards the risks involved in accepting BTC as a currency. However, knowing which market reacts the most quickly to new information could prove to be beneficial to regulators who aim to implement a particular BTC price, and, as a result, prevent any arbitrary prices, and eventually stabilize the financial market.
Abstract Higher media coverage and stronger investor interest in cryptocurrency market may create closer linkages with traditional assets, leading to deteriorated diversification benefits. Cryptocurrencies have recently emerged as an alternative digital asset class; however, very little is known about their portfolio performances. In this study, we investigate the timeâvarying investment benefits of cryptocurrencies for stock portfolios using a correlationâbased conditional diversification benefits (CDB) measure. We construct six portfolios consisting of cryptocurrencies, developed and emerging equity markets and find that the timeâvarying correlations between cryptocurrencies and stock markets are generally low. However, the level of correlations significantly increases in turbulent periods, such as Brexit referendum and Coincheck hack. The dynamic CDB measures suggest that adding cryptocurrencies to equity market portfolios enhances portfolio diversification; however, the benefits of diversification have diminished after late 2017. Our results offer significant insights and potential implications for market participants.
Abstract This article examines the connectedness and information spillover in the ElectricityâCrypto Network (ECN) system. The Bitcoin and Ethereum markets are studied due to the level of electricity demand for active trading and mining in the three leading crypto mining economies (United States, China, and Japan). Among other findings, the leading net transmitter of information is the return of the Bitcoin market while the demand for electricity in the U.S. and Japan are the leading net information receivers in the ECN system. In a nutshell, the return and trading volumes of the cryptocurrency markets are net information transmitters while the markets' volatility and the demand for electricity in the U.S., China, and Japan are net information receivers in the system. As a policy relevance, given the favourable developments in these crypto markets, greener sources of electrical energy are expedient to mitigate emissions while mining these coins. This will reduce the impact of human activities on the climate.
Kais Tissaoui, Taha Zaghdoudi, Khaled issa Alfreahat
This paper examines two competing hypotheses, that is, mixture of distribution hypothesis (MDH) and sequential information arrival hypothesis (SIAH) in the cryptocurrency market using high-frequency data. Specifically, we attempt to test the explanatory power of intraday public information arrival for Bitcoin returns and volatility over the period from January 1, 2019 to May 16, 2019. Based on AR (2)-PGARCH (1.1. ĂĂ´), the empirical results reveal the following: First, we find more evidence to support the MDH than the SIAH since the current trading volume participates to absorb the persistence of Bitcoin volatility stronger than the lagged trading volume. Second, solid evidence of the instantaneous effect of intraday trading volume on intraday Bitcoin returns is verified more than the lagged effect, which supports the MDH rather than the SIAH.
OlaOluwa S. Yaya, Xuan Vinh Vo, Ahamuefula E. Ogbonna, Adeolu O. Adewuyi
Abstract This paper empirically provides support for fractional cointegration of high and low cryptocurrency price series, using particularly, Bitcoin, Ethereum, Litecoin and Ripple; synchronized at different high time frequencies. The difference of high and low price gives the price range, and the rangeâbased estimator of volatility is more efficient than the returnâbased estimator of realized volatility. A more general fractional cointegration technique applied is the Fractional Cointegrating Vector Autoregressive framework. The results show that high and low cryptocurrency prices are actually cointegrated in both stationary and nonâstationary levels; that is, the range of highâlow price. It is therefore quite interesting to note that the fractional cointegration approach presents a lower measure of the persistence for the range compared to the fractional integration approach, and the results are insensitive to different time frequencies. The main finding in this work serves as an alternative volatility estimation method in cryptocurrency and other assets' price modelling and forecasting.