Bu çalışmanın amacı, Covid-19 sürecinde bir kripto para birimi olan Bitcoin’in; seçilmiş alternatif yatırım araçları (Brent Petrol, Altın, Etherium) ve seçilmiş göstergeler (Dow Jones, VIX, Covid-19 Google Trend aramaları) ile arasındaki uzun dönemli ilişkileri analiz etmektir. Bu amaç doğrultusunda çalışmada 23/02/2020-16/01/2022 dönemine ait haftalık verilerden yararlanılmıştır. Bitcoin fiyatları ile seçilmiş finansal göstergeler arasındaki uzun dönemli ilişkinin varlığı ise, ARDL Sınır Testi aracılığıyla sınanmıştır. Yapılan analiz neticesinde Bitcoin fiyatı ile seçilmiş finansal göstergeler arasında uzun dönemli bir ilişkinin varlığı tespit edilmiştir. Uzun dönem katsayılarından elde edilen sonuçlara göre, Bitcoin fiyatını en fazla etkileyen göstergelerin, Altın ve VIX endeksi olduğu bulgulanmıştır. Diğer yandan, Bitcoin fiyatları ile Brent Petrol ve Dow Jones endeksi arasında uzun dönemli bir ilişkiye rastlanılmamıştır. Kısa dönem hata düzeltme modelinin sonuçlarına bakıldığında, cari dönemde olası bir şok veya olumsuz senaryo neticesinde meydana gelecek dengesizliğin veya sapmanın bir sonraki dönemde (gelecek haftada veya haftalarda) %51’lik kısmının telafi edilebileceği bulgulanmıştır.
Abstract Based on asset pricing theory, we posit and find that equity markets and cryptocurrency markets share a common fundamental. Our cointegration tests show that the most important asset pricing primitive, consumption, can serve as the common fundamental. We further show that additional macroeconomic factors, as well as uncertainty and sentiment, all play a role in explaining the deviation from fundamentals. To understand the linkage between equity markets, cryptocurrency markets, and the macroeconomy, we suggest the following three channels: (i) portfolio allocation decisions, (ii) intermarket order flows, and (iii) technological adaption expectations.
2007 yılında ABD`de başlayan `Mortgage Krizi` çok geçmeden bütün dünya ekonomisini 2008 yılında etkisi altına almıştır. Ülke ekonomilerinde oluşan kriz, finansal piyasalarda çöküş ve dijital ortamlarda gelişmeler sonucunda 2009 yılında ilk kripto para birimi olan Bitcoin’in ön plana çıkmasına yol açmıştır. Günümüzde yüksek piyasa değerine sahip olması, merkezi yönetim sisteminin olmaması, anonimliği ve sağladığı birçok avantajlar bakımından dikkatleri üzerine çekmiş ve geleneksel yatırım araçlarına rakip olup olmaması her zaman tartışma konusu olmuştur. Bu yüzden araştırmacılar tarafından Bitcoin ile ilgili farklı yaklaşımlar ve yöntemler kullanılarak çalışmalar yapılmıştır. Bu çalışmanın temel amacı Bitcoin fiyatı ile Yükselen Piyasa Ekonomileri`nin borsa endeksleri ve diğer önemli değişkenler arasında ilişkinin boyutu ve yönünün belirlenmesidir. Ayrıca Bitcoin`in geleneksel yatırım araçlarına alternatif mi yoksa bir balon mu olduğunu belirlemek amaçlanmıştır. Uygulanan testler sonucunda bazı Yükselen Piyasa Ekonomileri ülkelerinde Bitcoin ile borsa endeksleri ve diğer değişkenler arasında istatistiksel olarak anlamlı ilişki bulunmuştur ve o ülkeler için Bitcoin`in alternatif yatırım aracı olduğu sonucuna ulaşılmıştır.
Owing to the high penetration of cryptocurrencies in the Turkish Economy, we sought to determine whether cryptocurrencies as represented by Bitcoin has become a global risk for the Turkish Lira. To accomplish this, we model the Turkish Lira exchange rate returns volatility using threshold GARCH-M with Bitcoin as an exogenous covariate. Bitcoin was found to be a contributor to Turkish forex volatility up until January 2018 when the ‘ongoing’ currency crisis started. Bitcoin, however, lost its volatility contributory power from January 2018. This result is robust to the inclusion of CBOE-VIX, iShares MSCI Turkey EFT, and the dollar-lira interest rate differential as control variables.
Hassan Obeid, Aymen TURKI, Ahmed Jeribi, Sahar Loukil
This study examines information dissemination across G7 markets for Bitcoin, stocks, and oil before and during the COVID-19 pandemic. We used a vector autoregressive model and impulse response function to analyze data. Our findings suggest that the pandemic has had a considerable effect on increasing the directional causalities and time-varying connectedness between Bitcoin, oil, and G7 stock indices during the crisis. Bitcoin significantly influences oil and stock returns during the pandemic. Moreover, the response of Bitcoin to shocks in stocks returns is more pronounced for France, Germany, Italy, and the United Kingdom than Japan, the United States, and Canada. The results could aid investors with portfolio diversification and hedging strategy in different G7 stock markets.
The emergence of cryptocurrencies futures market is an innovative platform for prudent investors to hedge risk contained in their portfolio. However, the dicey environment of cryptocurrencies and mandatory requirement of Ind AS 39 has aroused the need for estimating hedging effectiveness of their futures market. This treatise is an attempt to investigate the hedging effectiveness of Bitcoin, Ethereum, XRP and Bitcoin Cash covering the period from June 2018 to May 2022. The interconnectedness of their spot and futures markets is initially studied using Johansen cointegration test, dynamic conditional correlation model, vector error correction model and block exogeneity Wald test. Their empirical results indicate interconnectedness in these markets having significant long-term relationship; persistent volatility correlations; significant unidirectional long-term causality from futures to spot; and bidirectional short-term causality in all cryptocurrencies. So, investors can hedge their risk by engaging position in cryptocurrencies’ futures. The OLS, VECM, GARCH and TARCH methodologies are applied to estimate static optimal hedge ratios and their estimates indicate that all cryptocurrencies have negative and significant ratios except XRP. The symmetric as well as asymmetric diagonal VECH and diagonal BEKK methodologies are applied to estimate dynamic-hedge ratios and their estimates depict negative mean dynamic-hedge ratios of all cryptocurrencies except XRP. These estimations imply that investors having long position in spot contracts of Bitcoin, Ethereum and Bitcoin Cash should hedge by taking short position in their futures contracts, respectively. However, XRP investors should hedge by taking long position in XRP future contracts. The empirical results clearly indicate the outperformance of static hedge strategies over dynamic hedge strategies as variance reduction framework of Ederington favours static OLS hedge strategy and the risk–return framework of Howard and D’Antonio favours static VECM hedge strategy for all cryptocurrencies. So, the long-run considerations have played a more crucial role as compared to short-run information. These findings may guide investors having different objective functions in understanding the effectiveness of different hedge strategies and their usage for achieving their objective functions. Policymakers, treasurers and auditors may also be benefitted from the insights provided in the present treatise on different estimation methodologies for hedging effectiveness.
Purpose The mining process is essential in cryptocurrency networks. However, it consumes considerable electrical energy, which is undoubtedly harmful to the environment. In response, energy-conserving cryptocurrency projects with reduced energy requirements or based on renewable energies have been developed. Recently, the COVID-19 pandemic and the Russian invasion of Ukraine ignited an unprecedented upheaval in financial products, especially in cryptocurrency and energy markets. Therefore, the paper aims to explore the response of these energy-conserving cryptocurrencies to the COVID-19 pandemic and the Russia–Ukraine conflict. Design/methodology/approach This paper investigates the response of these energy-conserving cryptocurrencies to the COVID-19 pandemic and the Russia–Ukraine conflict. Their competitiveness is compared with conventional ones by analyzing their efficiency through multifractal detrended fluctuation analysis and automatic variance ratio during the COVID-19 and Russian invasion periods. Findings The empirical results show that all investigated energy-conserving cryptocurrencies negatively responded to the pandemic and positively reacted to the Russian invasion. On the other hand, all conventional cryptocurrencies reacted negatively to the COVID-19 pandemic and the amid-Russian attack. Besides, Bitcoin and SolarCoin were the least inefficient before the outbreak of COVID-19. Nevertheless, the Ethereum market became the most efficient after the pandemic spread. Similarly, the efficiency of Ripple was the most significant during the conflict between Russia and Ukraine. The energy crisis caused by Russia benefited the efficiency of the studied energy-conserving cryptocurrencies. Practical implications This research is of interest to investors seeking opportunities in these energy-conserving cryptocurrencies and policymakers working to implement reforms to improve their market efficiency and promote long-term financial market growth. Originality/value To the best of the authors' knowledge, the behavior of cryptocurrencies based on renewable and reduced energy during the recent conflict between Russia and Ukraine has not been explored.
Our study collected and synthetized the existing knowledge on portfolio diversification, hedge, and safe-haven properties in cryptocurrency investments. We sampled 146 studies published in journals ranked in the Association of Business Schools 2021 journals list, considering all fields of knowledge, and elaborated a systematic literature review along with a bibliometric analysis. Our results indicate a fast-growing literature evidencing cryptocurrencies’ ability to hedge against stocks, fiat currencies, geopolitical risks, and Economic Policy Uncertainty (EPU) risk; also, that cryptocurrencies present diversification and safe-haven properties; that stablecoins reveal unstable peg with the US dollar; that uncertainty is a determinant for cryptocurrency returns. Additionally, we show that investors should consider Gold, along with the European carbon market, CBOE Bitcoin futures, and crude oil to hedge against unexpected movements in the cryptocurrency market.
Emmanuel Joel Aikins Abakah, Guglielmo Maria Caporale, Luis A. Gil‐Alana
This paper assesses the impact of US policy responses to the Covid-19 pandemic on various technology-related assets such as cryptocurrencies, financial technology, and artificial intelligence stocks using fractional integration techniques. More precisely, it analyzes the behavior of the percentage returns in the case of nine major coins (Bitcoin—BITC, Stella—STEL, Litecoin—LITE, Ethereum—ETHE, XRP (Ripple), Dash, Monero—MONE, NEM, Tether—TETH) and two technology-related stock market indices (the KBW NASDAQ Technology Index—KFTX, and the NASDAQ Artificial Intelligence index—AI) over the period 1 January 2020–5 March 2021. The results suggest that fiscal measures such as debt relief and fiscal policy announcements had positive effects on the series examined during the pandemic, when an increased mortality rate tended instead to drive them down; by contrast, monetary measures and announcements appear to have had very little impact and the Covid-19 containment measures none at all.
Ahmed Bouteska, Petr Hájek, Mohammad Zoynul Abedin, Yizhe Dong
This study aims to examine whether the prices and returns of two cryptocurrencies, Dogecoin and Ethereum, are affected by Twitter engagement following the COVID-19 pandemic. We use the autoregressive integrated moving average with explanatory variables model to integrate the effects of investor attention and engagement on Dogecoin and Ethereum returns using data from December 31, 2020, to May 12, 2021. The results provide evidence supporting the hypothesis of a strong effect of Twitter investor engagement on Dogecoin returns; however, no potential impact is identified for Ethereum. These findings add to the growing evidence regarding the effect of social media on the cryptocurrency market and have useful implications for investors and corporate investment managers concerning investment decisions and trading strategies.
As a great innovation in virtual currency, bitcoins have the possibility to survive perpetually, although they are like gigantic bubbles. However, no matter whether bitcoins could survive or not, the technology used by bitcoins will exist and develop. There is a great possibility for bitcoins to be served in the intending currency, being issued, supported, and controlled by the government. Consequently, the research for bitcoins is meaningful. To explore the time relationship of bitcoins and give a prediction about the future price based on the given data, ARIMA and GARCH models are used in this paper. Although both of the two models failed to provide the accurate forecasts at the end of this research, they still proved the correlation within time series of bitcoins.
Bitcoin establishes itself as an investment asset and is often named the New Gold. This study, however, shows that the two assets are different in univariate and multivariate aspects. First, we construct GARCH, APARCH and APARCH-in-Mean models to analyze and compare conditional variance properties of Bitcoin and Gold, and find Bitcoin does not have the significant inverse leverage effect as Gold. Then we apply the BEKK-GARCH model to estimate time-varying conditional correlations between Bitcoin and Gold with other major market indexes. The results show that Bitcoin can not hedge the market risk, especially when a crash occurs. So we conclude that Bitcoin and Gold feature fundamentally different properties as assets and linkages to equity markets.
Yatırım, tasarruf sahiplerinin finansal sürdürülebilirliğin güvence altına alınmasını sağlayan önemli bir araçtır. Bu nedenle yatırım kararlarının belirlenmesi ve portföy oluşturma süreçleri güncelliğini yitirmeyen bir araştırma konusu olagelmiştir. Bu çalışmada son yıllarda çok sayıda tartışmaya konu olan Bitcoin’in portföyler için doğru bir alternatif olup olmadığı tartışılmaktadır. Portföyler oluşturulurken çeşitliliği artırmak için Dolar, Euro, Bitcoin, Bist100 ve Altın alternatif yatırım araçları ele alınmıştır. Portföyler eşit oranlı bir dağılıma sahip olacak şekilde beş yatırım aracının olası tüm kombinasyonlarından oluşturulmuştur. Yatırım kararı, çok kriterli karar verme problemi olarak ele alınmış ve değerlendirme için yıllık getiri göstergesi, yıllık değişim oranı ve varyans katsayısı olacak şekilde üç kriter belirlenmiştir. Kriterlerin ağırlıkları nesnel bir yaklaşım olan MEREC yöntemiyle hesaplanmış ve alternatif seçimi VIKOR yöntemiyle gerçekleştirilmiştir. Çalışmada, Bitcoin’in portföy çeşitlendirmek için uygun bir alternatif olduğu sonucuna ulaşılmıştır.
Many investors include cryptocurrencies as potential investment tools in their portfolios. Previous studies have mostly analyzed Bitcoin regarding its hedge and safe haven features. Although the cryptocurrency market has expanded far beyond Bitcoin, few studies have examined the interaction among all other cryptocurrencies and conventional financial assets. For this purpose, as the dependent variable, we included the cryptocurrency index to represent the cryptocurrency market, whereas international stocks, bonds, United States (US) dollars, gold, and commodities as independent variables in the analysis. The interactions among the variables were analyzed using the Granger causality tests. The analysis results revealed a two-way causality relationship between the cryptocurrency market and the bond markets, indicating that the cryptocurrency index can be used to predict bond prices and vice versa.
Tolgahan Tuglu, Canan Dağıdır Çakan, Mehmet Hanifi Ateş, Aleyna Uca
Cryptocurrencies have been attracting a significant amount of attention in the world since they were first launched in 2009. Pretending to be a decentralized finance solution, it brought out a new era in technology called blockchain. Even though the benefits did not come into action in daily routines for many to be aware of, the market and its variety kept growing. On the other hand, there are also a lot of concerns and unpredictability about the future of this technology. Especially the high energy consumption while generating blocks for mining cryptocurrencies and completing transactions is commonly being criticised. In this study, blockchain technology and the basics of mining and validation procedures such as proof of work (PoW) and proof of stake (PoS) processes will be explained, and the environmental effects of bitcoin mining will be investigated. In the perspective of environmental sustainability of cryptocurrencies, the improvement in usage of renewable energy and its side benefits will be overviewed for a better prediction on the blockchain technology future.
Abstract The high energy consumption and carbon footprints have raised environmental and sustainable concerns of green investors and policymakers. This study explores comovements between three green and socially responsible financial assets, S&P global clean energy index (GCEI), S&P green bonds index (GB), DJ sustainability world index (DJSWI) and four cryptocurrency uncertainty/attention indices cryptocurrency policy uncertainty index, Central Bank Digital Currencies Uncertainty Index, Central Bank Digital Currencies Attentions Index and Index of Cryptocurrency Environmental Attention using the bivariate wavelet coherence approach. The findings show that GCEI, GB, DJSWI returns have consistent positive comovement with all cryptocurrency uncertainty/attention indices in the medium‐term, suggesting their time‐varying leading role. Evidence of negative coherences shows that higher cryptocurrency uncertainties/attentions lead to lower green financial asset returns, reflecting the adverse impact of higher uncertainties/attention on the trust of green and sustainable investors. The above empirical findings offer up‐to‐date insights for guiding policymakers, and regulators, enabling them in environmental policy development. Furthermore, socially responsible investors can make better investment judgments by considering the environmental concerns in the cryptocurrency marketplaces.
Abstract Cryptocurrencies have emerged as an important investment avenue in the past few years. Investors are increasingly interested in these currencies amid surging financial returns. In this context, understanding market efficiency of cryptocurrency has become very crucial for investors and academicians. The price–volume framework is a popular approach in financial economics to understand the market efficiency of stocks in the stock markets. Therefore, this article examines the market efficiency of cryptocurrencies through price–volume framework to understand whether crypto market is predictable. Towards this objective, data on both return and trading volume (TV) of the top eight cryptocurrencies are used for the period 8 August 2015–20 October 2022. As an empirical method, both linear and non‐linear causality models are used to validate the hypothesis. Our results confirm that TV cannot predict the cryptocurrencies' return, thereby validating the market efficiency hypothesis. Furthermore, we divide the sample according to the structural break period. The result from the post‐break period analysis also confirms the presence of market efficiency in the recent period for all currencies, barring XRP, XMR and DASH.
Bitcoin prices are highly volatile and have extreme upper tails of the return distributions. One important component of Bitcoin price jumps is that it does not follow a normal distribution. This present study aims to reduce the extreme value data available on Bitcoin into simple clusters based on extreme value returns. The study first measures the excessive volatility and then estimates the extreme value returns of Bitcoin between November 2013 and August 2022 to achieve this objective. For robustness checks, extreme value returns are estimated using both the Rogers and Satchell (RS) and the Variance Ratio (VRatio) estimators that embed jumps in the model. Further, K-means clustering is used to form clusters based on the estimated Bitcoin’s extreme value returns as the probable good days (extreme days), medium days, and bad days. The study observes that K-means clustering can explain 65 percent point return variability. The study findings will be highly useful for crypto investors, policymakers, and future studies in data mining.
Bitcoin and other cryptocurrencies are emerging markets that are growing more and more important in the financial world. Since the definition of money has changed and its price has fluctuated, cryptocurrencies like Bitcoin and others have grown in popularity. In this study, we suggest the use of machine learning technologies and readily accessible social media data for forecasting the price movement of the Bitcoin market. We used sentiment analysis and machine learning techniques to extract tweets from Twitter postings to examine the relationship between bitcoin price changes and tweet sentiment. We used a variety of machine learning methods to create a prediction model and insightful analysis of future market values. We use five distinct machine learning models, including Support Vector Regression (SVR), Prophet, An Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and XGBoost. Every model was tested on the last 30% of the data after training on the first 70%. The models' Root Mean Square Errors (RMSE) are compared. The expansion of the collection of significant characteristics retrieved from textual data using sentiment analysis employing long short-term memory is another way that this work adds to the body of knowledge on directed bitcoin price returns forecast (LSTM). The findings demonstrate that cryptocurrency markets may be predicted using machine learning and sentiment analysis, however other coins may be predicted using only Twitter data. The most impressive outcome from the LSTM model.
Reza Nourmohammadi, Mahdi Arabian, Masoumeh Ghorbanpour, Mohammad M. Nazemi · 5 authors
As a cutting-edge technology, non-fungible tokens (NFT) have attracted a great deal of attention since 2021. Considering the numerous applications of these non-interchangeable digital assets in various industries and their tradability, NFTs have become an important element of many investors’ portfolios. Therefore, in order to evaluate NFTs and determine their main value, different tools must be used. The purpose of this study is to understand the dominant factors that influence the valuation of NFT assets. The purpose of this paper is to present a novel methodology for constructing a utility valuation model for NFTs as a whole. We will be able to analyze and diagnose the dynamics and performance of NFT markets using this model. We developed three models for scoring NFTs in this study, which can be used to speed up the evaluation process in three different dimensions.