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
This paper examines the volatility spillovers between Bitcoin market and US banking industry using unrestricted BEKK-GARCH model. The results show that there is a strong short-term volatility spillover effect in the two markets. However, Bitcoin trading volatility process weakens the short-term volatility spillover effect from Bitcoin market to banking industry in the United States and the volatility of banking industry returns (i.e. volatility of operational results) weakens the short-term volatility spillover effect from banking industry to Bitcoin market mainly due to inability of adoption in the short-run. Moreover, there is a significant and (positive) long-term volatility spillover effect from Bitcoin market to banking industry. This remarkable observation reveals that there is a possibility of banking industry adopting Bitcoin operation as a part of banking product portfolio development in the long-run. As such, imposition of any tax or trading restriction (e.g. price bands, transaction cost, tax etc.) on Bitcoin market will adversely impact the performance of banking industry in the long-run. The nature of the impact and its timing are of utmost importance for the government and policymakers, particularly in case of economic planning and restructuring of banking and financial services industry.
In this study Non‐Linear forecasting models have been implemented to forecast the seven major cryptocurrencies. To the best of the authors knowledge, this is the first study to forecast the cryptocurrencies chaotic co‐movement forecasting using non‐linear models like Neural networks. The study finds that LSTM yields better result for lags 0 and 0‐3 and for large lags 0‐7, the ANN is the best. Further study confirms that predictions using variables like volume is not suitable for forecasting in any case. The findings of the study will impact Policy makers and investors.
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.
Today, commodities are exposed to ever-increasing price volatilities due to extreme market uncertainties linked with financialization. The paper addresses a timely question of whether cryptocurrencies are hedge and safe-haven for commodities. We focus on this literature gap by using individual commodities from four groups, including metal, agriculture, precious metal, and energy. Further, we also consider four major cryptocurrencies, namely, Bitcoin, Ethereum, Litecoin, and Ripple for our analysis. Our findings show the functional role of cryptocurrencies as hedge and safe-haven for individual commodities. Moreover, the underlying properties are persistent during the crisis period.
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.
What is the driving force of the evolution of monetary systems in the longrun? Based on an in-depth analysis of economic history and the findings of contemporary studies, Prof. S. Andryushin argues that it is the perpetual rivalry between centralization and decentralization. This article juxtaposes arguments for and against such a viewpoint. In particular, I assert that centralization or decentralization per se cannot safeguard financial stability, nor secure optimality of monetary policy. Both trends need to be assessed alongside with concomitant political and economic factors as well as institutional environment. Against this backdrop, the ongoing trend towards decentralization associated with cryptocurrencies is so far unlikely to remedy all the drawbacks of the contemporary monetary system.
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.