Muhammad Abubakr Naeem, Mudassar Hasan, Muhammad Arif, Syed Jawad Hussain Shahzad
We compare the hedging, safe-haven, and diversification potential of gold and Bitcoin for different investment styles and industry portfolios in the United States. We find that gold is at least a weak hedge for the style and industry portfolios except for utilities, energy, and telecom. The hedging potential of gold is comparatively higher for large-cap portfolios, whereas Bitcoin offers minimal hedging effectiveness. However, Bitcoin shows hedging potential for the noncyclical industries. Although investors need a higher amount of investment to hedge the downside risk using gold, it still is a superior hedging instrument compared with Bitcoin. Finally, the analysis using the conditional diversification approach shows that gold is a superior and stable diversifier for style and industry portfolios. Overall, our findings provide evidence of superior safe-haven and hedging potential of gold over Bitcoin.
Bitcoin being a safe-haven asset is one of the traditional stories in the cryptocurrency community. However, during its existence and relevant presence, i.e., approximately since 2013, there has been no severe situation on the financial markets globally to prove or disprove this story until the COVID-19 pandemic. We study the quantile correlations of Bitcoin and two benchmarks—the S&P 500 and VIX—and make comparison with gold as the traditional safe-haven asset. The Bitcoin safe haven story is shown and discussed to be unsubstantiated and far-fetched, while gold comes out as a clear winner in this contest even when a broader cryptocurrency index (CRIX) is considered.
This paper compares a number of stochastic volatility (SV) models for modeling and predicting the volatility of the four most capitalized cryptocurrencies (Bitcoin, Ethereum, Ripple, and Litecoin). The standard SV model, models with heavy-tails and moving average innovations, models with jumps, leverage effects and volatility in mean were considered. The Bayes factor for model fit was largely in favor of the heavy-tailed SV model. The forecasting performance of this model was also found superior than the other competing models. Overall, the findings of this study suggest using the heavy-tailed stochastic volatility model for modeling and forecasting the volatility of cryptocurrencies.
Bitcoin being a safe haven asset is one of the traditional stories in the cryptocurrency community. However, during its existence and relevant presence, i.e. approximately since 2013, there has been no severe situation on the financial markets globally to prove or disprove this story until the COVID-19 pandemics. We study the quantile correlations of Bitcoin and two benchmarks -- S\&P500 and VIX -- and we make comparison with gold as the traditional safe haven asset. The Bitcoin safe haven story is shown and discussed to be unsubstantiated and far-fetched, while gold comes out as a clear winner in this contest.
For the past couple of years, Machine learning and trading helped by artificial intelligence has drawn growing interest. Here, the approach is used to test the hypothesis that the inefficiency of cryptocurrency industry can be exploited in order to produce anomalous revenue. For the duration between Nov. 2015 and Apr. 2018, daily data for 1, 681 crypto currencies were analyzed. Simple trade techniques supported by state-of -the-art machine learning algorithms are seen to outperform the traditional benchmarks. The results obtained imply that non-trivial, but fundamentally simple, algorithmic processes will help to predict the short-term future of the cryptocurrency market. The popularity of cryptocurrencies had skyrocketed in 2017 due to several consecutive months of super-exponential growth of market capitalization. There are over 1,500 currently recorded cryptocurrencies actively trading today with the cryptocurrencies sitting on more than $300 billion [2], and a total market capitalization of over $800 billion in January 2018. According to a recent survey, between 2.9 and 5.8 million privates as well as institutional investors are in the numerous investment networks and access to markets has become easier over time. In a number of online markets, major crypto currencies can be purchased using fiat currency, and then used in order to purchase less known crypto currencies. The average trading amount is globally exceeding $15bn. About 170 money market funds had been invested in cryptocurrencies since 2017, and Bitcoin futures are launched in order to satisfy the Bitcoin trading and hedging demand for the market. The main objective of the work is to predict the Bitcoin prices, one of the most popular and widely used cryptocurrency which is a source of attraction for many investors as a source of profit or investment. But the market for the cryptocurrencies been volatile since the day it was first introduced. So, the approach towards the survey is to use LSTM RNN and use the available dataset and train the model to give the highest possible accuracy and to provide a real-time price of the Bitcoin for the following days.
In this paper, it has been aimed to reveal the possible effects of Covid-19 Coronavirus epidemic on stock markets. In the analysis using daily data between 23 January 2020 and 13 March 2020, possible effects on stock markets has been investigated with Maki (2012) cointegration test using both Covid-19 daily total death and Covid-19 daily total case. According to the results obtained, all stock markets examined with total death act together in the long run. It has been understood that total cases have cointegration relationship of SSE, KOSPI and IBEX35 and do not have cointegration relationship with FTSE MIB, CAC40, DAX30. In this regard, it is considered as one of the optimal option for investors to avoid investments in stock markets, turn to investment in gold markets, which is the safe investment port of each crisis period in long run. Also, considering the possibility of turning all life into an internet environment, turning to cryptocurrencies is seen as another alternative option for investors. In this direction, it will be the preference of investors to turn to derivative markets and to the stock markets of countries where Covid-19 is relatively rare to avoid risk.
Abstract In recent years, the tendency of the number of financial institutions to include cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading ( e . g ., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.
Cem Çağrı Dönmez, Ahmet Fatih Dereli, Muhammed Bilal Horasan, Cagri Yıldız
The focus of this research is to describe and discuss future blockchain technology in relation to different forms of digital cryptocurrencies by investigating distinct characteristics and common features of cryptocurrencies on the market. This research explores significant relationships between the major cryptocurrencies on the complex cryptocurrency market ecosystem, particularly Bitcoin and the most prominent altcoins based on historical market capitalization data for the last two years. In this work cross-correlations between different cryptocurrencies are examined in terms of changes in the market capitalization value. For the comparative analysis minimum spanning tree (MST) and hierarchical structure tree (HST) methods are applied in the context of economic behaviour of cryptocurrencies with regard to global cryptocurrency market trends.
M. Akhil Sai, K. Sarath Chandra Sai, M. Manu Koushik, K. Gowri Raghavendra Narayan
ML and AI-helped exchanging have pulled in developing enthusiasm for as far back as not many years.We examine day-by-day information for different digital currencies over some stretch of time. We show that straightforward exchanging methodologies helped by innovative AI calculations outflank standard benchmarks. We have picked two Machine Learning Algorithms to play out a Comparative Study to foresee cost of a Bitcoin; we have utilized Decision tree regressor and LSTM Algorithms and watched execution of every calculation as far as anticipating the cost of Bitcoin. We saw that Decision tree regressor gives progressively effective and precise outcomes when contrasted with others.
This study measures the volatility of cryptocurrency by utilizing the symmetric (GARCH 1, 1) and asymmetric (EGARCH, TGARCH, PGARCH) model of GARCH family using a daily database designated in different digital monetary standards. The results for an explicit set of currencies for entire period provide evidence of volatile nature of cryptocurrency and in most of the cases, the PGARCH is a better-fitted model with student’s t distribution. The findings show positive shocks heavily affected conditional volatility as a contrast with negative stuns. Those additional analyses can be provided further support their findings and worthwhile information for economic thespians who are engrossed in adding cryptocurrency to their equity portfolios or are snooping about the capabilities of cryptocurrency as a financial asset.
This study empirically investigates the effects of crypto-currencies trading on the energy consumption as an important consequence of blockchain technology on climate change. In this article, we use the data of Bitcoin trading volume as well as all crypto-currencies trading volumes for the period going from 2014M1 to 2017M12 to investigate the effects on the primary energy consumption. Our empirical results show a positive correlation between crypto-currencies trading volumes and the energy consumption. Moreover, the crypto-currencies trading volume has a Granger-causality to energy consumption in the period of study indicating that these two variables have a long-run co-integration. In other words, our findings show a significant positive (and increasing) influence of cryptocurrency activities on the energy consumption in both short-run and long-run. This study investigates one step further in examining the effects of residuals of the crypto-currencies trading volume on the residuals in energy consumption to confirm that a higher trading volume in cryptocurrencies might cause a higher energy consumption. Our findings show a negative influence of the trading of crypto-currencies - precisely, the higher the crypto-currency activities are, the higher the energy consumption is, affecting therefore the environment.Keywords: Crypto-currencies, Environment; Energy consumption; Innovation.JEL Classifications: Q40, Q51, Q54, Q55, Q56DOI: https://doi.org/10.32479/ijeep.9258
Finansal piyasaların ilgi noktasını oluşturan kripto para birimlerinden bitcoin’in para birimi olarak yayılması ve kullanılmasından sonra herkesin aklında, bitcoin’in bir yatırım aracı olarak ya da hedge enstrümanı olarak değerlendirilip değerlendirilemeyeceği sorusu yer almaya başlamıştır. Çalışmada kripto para birimlerinden en çok işlem hacmine sahip olan bitcoin’in alternatif yatırım araçları arasında uzun dönemli ilişkilerini ortaya koymak için istatistiki analiz yapılmış ve bununla ilgili bulgular tartışılmıştır. Birçok kripto para olmasına karşın Bitcoin’in her açısından önde gelmesi nedeniyle, bitcoin ile alternatif yatırım araçları arasında bir eş bütünleşmenin olup olmadığı ARDL testi ile ortaya koyulmaya çalışılmıştır. Çalışmada Bitcoin ile alternatif yatırım araçları arasında geniş kapsamda ele alan salt bir çalışma görülmediğinden dolayı bu çalışmanın yapılmasına karar verilmiştir.
Jéssica Paule-Vianez, Camilo Prado Román, Raúl Gómez-Martínez
Purpose The goal of this work is to determine whether Bitcoin behaves as a safe-haven asset. In order to do so, the influence of Economic Policy Uncertainty (EPU) on Bitcoin returns and volatility was studied. Design/methodology/approach It is evaluated whether, when compared with the evolution of EPU, Bitcoin's returns and volatility show behaviours typical of safe havens or rather, those of conventional speculative assets. When faced with an increase in EPU, safe havens – such as gold – can be expected to increase their returns and volatility, while conventional speculative assets will increase their volatility and reduce their returns. This study uses simple linear regression and quantile regression models on a daily data sample from 19 July 2010 to 11 April 2019, to analyse the influence of EPU on the returns and volatility of Bitcoin and gold. Findings Bitcoin's returns and volatility increase during more uncertain times, just like gold, showing that Bitcoin acts not only as a means of exchange but also shows characteristics of investment assets, specifically of safe havens. These findings provide useful information to investors by allowing Bitcoin to be considered as a tool to protect savings in times of economic uncertainty and to diversify portfolios. Originality/value This study complements and expands current research by aiming to answer the question of whether Bitcoin is a simple speculative asset or a safe haven. The most significant contribution is to show that Bitcoin is not a mere speculative asset but behaves like a safe haven.
2008 yılında temelleri atılmış olan Kiripto para kavramı, 2017 yılı Aralık ayı itibari ile 19.060 ABD dolarına ulaşmış ve tanınırlığını arttırmıştır. Bitcoin ve sayıları 2700’ü bulan diğer kripto paralar hızlı kazanç elde etmek isteyen yatırımcıların dikkatini çekmeyi başarmıştır. Bu kapsamda kripto paraların fiyatının nasıl ve ne yönde değişeceği birçok kesim tarafından araştırma konusu olmuştur. Bu çalışmanın amacı, Bitcoin, Ethereum, IOTA ve Ripple gibi farklı altyapısal özellikleri olan kripto paraların gelecek fiyatını geçmişte gerçekleşen fiyatlardan hareketle tahmin etmektir. Çalışmada Deng Ju-Long tarafından 1980’li yıllarda ortaya atılan gri sistem teorisi ile fiyat tahminlemesi yapılmıştır. Çalışmada kullanılan geçmiş fiyatlar 11 günlük süreci kapsamaktadır. Literatüre göre kısa sayılabilecek bu süre modelin diğer modellere görece üstünlüğünü göstermektedir. Elde edilen sonuçlara göre GM(1,1) model ve Rolling-GM(1,1) model sonuçlarının birbirine çok yakın hata oranlarıyla tahmin yaptıkları ve yapılan tahminlere ait hata oranlarının çok düşük olduğu görülmüştür.
This article analyzes the relationship between Bitcoin and the stock market by using a vector autoregressive model. To enhance the impulse response signal, the Sliding Window technique is applied. Study results show the relationship between Bitcoin and the stock market. First, the S&P 500 has a relatively significant effect on Bitcoin, while the influence caused by the S&P 500 is weak. In addition, after involving the Sliding Window technique, the effects caused by the standard deviation of the S&P 500 and the mean of the Dow Jones are remarkably strong on the mean of Bitcoin and the standard deviation of the S&P 500 has a comparatively significant effect on the standard deviation of Bitcoin as well. Generally, the S&P 500 and the Dow Jones indexes have an advantageous effect on Bitcoin. Financial investment can be made based on this model and conclusion.
Abstract Bitcoin is currently the leading global provider of cryptocurrency. Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage. In recent years, the Bitcoin network has attracted investors, businesses, and corporations while facilitating services and product deals. Moreover, Bitcoin has made itself the dominant source of decentralized cryptocurrency. While considerable research has been done concerning Bitcoin network analysis, limited research has been conducted on predicting the Bitcoin price. The purpose of this study is to predict the price of Bitcoin and changes therein using the grey system theory. The first order grey model (GM (1,1)) is used for this purpose. It uses a first-order differential equation to model the trend of time series. The results show that the GM (1,1) model predicts Bitcoin’s price accurately and that one can earn a maximum profit confidence level of approximately 98% by choosing the appropriate time frame and by managing investment assets.
International Journal of Psychosocial Rehabilitation - IJPR, is an editorial & peer-reviewed journal publication for mental health care providers, practitioners, nurses, consumers, and applied researchers, bearing ISSN: 1475-7192.