Leonardo H.S. Fernandes, Elie Bouri, JOSÉ W. L. SILVA, Lucian Bejan · 5 authors
We examine the price disorder and market efficiency of five cryptocurrencies (Bitcoin, BNB, Cardano, Ethereum, and XRP) before and during COVID-19 pandemic period. Using permutation entropy and Fisher information measure (FIM), we construct the Shannon-Fisher causality plane (SFCP) to map these cryptocurrencies and their respective locations in a two-dimensional plane and then apply sliding time window approach to study the temporal evolution of efficiency. All cryptocurrencies exhibit high but slightly varying informational efficiency during both periods. Cardano is the most efficient. These results might point to the increasing maturity and lower potential for price predictability, which matter to cryp-tocurrencies usage for liquidity risk diversification strategy.
The majority of NFTs utilize the Ethereum blockchain platform to facilitate smart contracts. In this paper, we execute various econometric analyses to determine if this technical dependence induces a financial linkage to the risk, return, and prices of assets. For robustness, we also test the same relation between Bitcoin and NFTs. Empirical analyses are conducted through SADF bubbles test, DCC-GARCH time-varying correlation analysis, Bootstrap causality tests and spillover analysis. According to the results of various price, return, and volatility analyses, we find that NFTs do not demonstrate idiosyncratic features in their price developments and thus they cannot be considered as a separate asset class. Additionally, NFTs do not possess a specific financial linkage with Ethereum from using its infrastructure. Finally, we suggest NFT investors use alternative financial instruments, rather than Ether and Bitcoin in portfolio diversification, due to the presence of significant time-varying relationships and interactions.
Sanal Kripto Para kavramı Bitcoin ile birlikte 2009 yılında dikkat çekmeye başlamış ve özellikle 2013 yılındaki fiyat artışı ile popülaritesi artmıştır. Sanal Kripto Para birimlerinin ilki ve öncüsü olan Bitcoin ile 2. En büyük piyasa değerine sahip olan Ethereum tasarımsal yapıları ve amaçları bakımından birbirlerinden oldukça farklıdır. Sanal bir para birimi olması için tasarlanan Bitcoin ile üzerinde akıllı kontratlar çalışmasına olanak vermek için tasarlanan Ethereum birçok alanda olduğu gibi iktisat alanında da oldukça dikkat çekmiş ve literatürde birçok çalışmaya konu olmuştur. Bu çalışmanın amacı Türkiye GSYH’sı, M2 Tanımı ile para arzı ve tüketici güven endeksinin Bitcoin ve Ethereum fiyatına olan etkilerini karşılaştırmalı olarak tahmin etmektir. Bu amaçla, Bitcoin ve Etherum’un Türk Lirası cinsinden fiyatlarını bağımlı değişken alan iki farklı model kurulmuştur. Kurulan modeller, Ocak 2016 ile Aralık 2020 dönemini kapsayan aylık veriler kullanılarak, zaman serisi analizi kapsamında, Johansen Eşbütünleşme Testi ve Tam Uyarlanmış En Küçük Kareler Yöntemi (FMOLS) kullanılarak uzun dönemde sınanmıştır. Yapılan analizin sonucunda hem Bitcoin hem de Ethereum’un Türk lirası cinsinden fiyatları, GSYH ve tüketici güven endeksi ile pozitif ilişkili, M2 para arzı ile negatif ilişkili bulunmuştur. Çalışmanın bulgularından bir diğeri ise toplam piyasa değeri Bitcoin’e göre daha düşük olan Ethereum’un tüm değişkenlerden daha çok etkilendiğidir. Elde edilen sonuçlar bu çalışmanın türetildiği Yüksek Lisans Tezindeki bulgular ile örtüşmekte ve birbirini desteklemektedir.
COVID-19'un başlangıcı, 2020'nin belirleyici olayı haline geldi ve kripto para birimleri de dahil olmak üzere tüm dünyadaki finansal piyasaları etkiledi. Bu süreçte, altın ve diğer emtialar gibi güvenli bir liman olarak görülmeye başlanan kripto para birimlerine ve diğer dijital varlıklara yatırıma olan ilgi arttı. Bu amaçla bu çalışmada Covid-19 sürecinde, altın ve petrol fiyatlarında meydana gelen şokların Bitcoin fiyatları üzerindeki asimetrik etkisi incelenmiştir. Çalışmada Doğrusal Olmayan Gecikmesi Dağıtılmış Otoregresif (NARDL) analiz yöntemi kullanılmıştır. Analizin sonucunda, uzun vadede altın fiyatlarında meydana gelen negatif şokların Bitcoin fiyatlarını olumlu etkilediği, petrol fiyatlarında meydana gelen negatif şokların ise Bitcoin fiyatlarını olumsuz etkilediği sonucuna ulaşılmıştır. Uzun vadede altın ve petrol fiyatlarında meydana gelen pozitif şokların ise Bitcoin fiyatları üzerinde istatistiki olarak anlamlı bir etkisinin olmadığı görülmüştür. Kısa vadede ise hem altın hem de petrol fiyatlarında meydana gelen pozitif şokların Bitcoin fiyatlarını olumlu etkilediği, negatif şokların ise olumsuz etkilediği tespit edilmiştir. Sonuç olarak, Bitcoin’in küresel yatırımcılar için finansal çeşitlendirmede ideal olabileceği ve yeni bir sanal altın olarak piyasalardaki yerini alabileceği görülmüştür.
In the last decade as a result of the changes in business landscape new payment systems have evolved. Some of the Consumers, business stakeholders, investors and individuals turned to different types of payment systems and virtual currencies for various reasons. Peer to peer architectured Bitcoin which uses a blockchain mechanism is one of these approaches that found place in our lives. In this study, a leading indicator focused data mining methodology has been followed in analyzing Bitcoin market value and bitcoin valuation. Several classification and clustering algorithms applied to the data following a literature review, pre-processing of the data and conceptual framework formation. Finaly performances of these supervised and unsupervised machine learning techniques with rules discovered have been compared, assessed and presented for this type of problem and research domains.
We investigate short- and long-term effects of U.S. economic policy uncertainty (EPU) on bitcoin, gold, and the implied US stock market volatility (VIX). We apply an autoregressive distributed lag model (ARDL) to monthly data. Our results suggest EPU significantly negatively (positively) impacts bitcoin over short (long) horizons. In contrast to the extant literature we find the magnitude of the effect of EPU on bitcoin returns weakens over longer horizons. Our empirical results provide a cautionary note for holders of asset portfolios that include bitcoin in their mix as a hedge against uncertainty.
Bu çalışma 2011 yılından itibaren temel olarak “belirsizlik” ve “ekonomi” anahtar kelimelerini içeren tweetlerin baz alınarak oluşturulduğu Twitter Bazlı Belirsizlik Endeksinin, son yılların gözde yatırım araçlarından olan kripto paraların volatilitesine etkisini incelemeyi amaçlamaktadır. Piyasa değeri en yüksek, Binance, Bitcoin, Cardano, Ethereum, Ripple ve Tether kripto paralar 18/01/2018- 11/07/2021 dönemi için günlük verilerle ARCH-GARCH ailesi modelleri ile incelenmiştir. Çalışmada öncelikle ortalama denklemi oluşturulan modellerin ARCH-GARCH modellerine uygunluğu sınanmış ve incelenen dönemde Bitcoin ve Ethereum için ARCH etkisinin olmadığı ancak Binance, Cardano, Ripple ve Tether için volatilite modellerinin kullanımının uygun olduğu bulgusu elde edilmiştir. Binance için GARCH (1,1), Cardano için GARCH-M (1,1), Ripple için ARCH (2) modeli volatiliteyi en iyi yakalayan model olarak seçilmiştir. Twitter Bazlı Belirsizlik Endeksinin bu modellerin hepsinde istatistiki olarak anlamlı ve pozitif bir etkiye sahip olduğu tespit edilmiştir. Bu sonuçlara göre bir sosyal medya platformu olan Twitter’da yer alan belirsizlik ve ekonomi içerikli tweetlerin kripto varlıkların volatilitesini etkilediğini söylemek mümkündür.
Sanjib Kumar Nayak, Sarat Chandra Nayak, Subhranginee Das
Artificial neural networks (ANNs) are suitable procedures for predicting financial time series (FTS). Cryptocurrencies are good investment assets; therefore, the effective prediction of cryptocurrencies has become a trending area of research. Capturing inherent uncertainties associated with cryptocurrency FTS with conventional methods is difficult. Though ANNs are the better alternative, fixing the optimal parameters of ANNs is a tedious job. This article develops a hybrid ANN through Rao algorithm (RA + ANN) for the effective prediction of six popular cryptocurrencies such as Bitcoin, Litecoin, Ethereum, CMC 200, Tether, and Ripple. Six comparative models such as GA + ANN, PSO + ANN, MLP, SVM, LSE, and ARIMA are developed and trained in a similar way. All these models are evaluated through the mean absolute percentage of error (MAPE) and average relative variance (ARV) metrics. It is found that the proposed RA + ANN generated the lowest MAPE and ARV values, statistically different as compared with existing methods mentioned above, and hence can be recommended as a potential financial instrument for predicting cryptocurrencies.
Coronavirus (COVID-19), which emerged as an epidemic in China in December 2019, has been recognized as a pandemic by the World Health Organization as of March 2020. Events regarding the coronavirus shocked the markets and were seen as a threat to the markets. In this context, this study aims to examine the effect of the COVID-19 on Bitcoin prices and precious metals which are seen as low-risk assets in global markets. In the study, the causality relationship between the daily number of COVID-19 cases approved by the WHO and Google trends, and the price series of Bitcoin, Gold, Silver, Platinum, Palladium was investigated to determine the effects of the developments in the course of the epidemic on the prices of Bitcoin and precious metals. Toda-Yamamoto causality test was performed in the study where daily data were used between 19.01.2020-31.03.2021. According to the findings, a causality relationship could not be determined between the number of COVID-19 cases with Bitcoin and precious metals while it was observed that the recognition of COVID-19 has a very strong causal effect on Bitcoin prices and the prices of other precious metals except silver. In addition, a reciprocal causality relationship has been identified between the confirmed COVID-19 cases and the recognition of COVID-19.
Bu çalışmanın amacı, en fazla ilgi gören kripto para birimleri arasında yer alan Bitcoin ile gelişmekte olan piyasalar arasında önde gelen Borsa İstanbul (BİST) endekslerinden BİST 100 (XU100), BİST Mali (XUMAL) ve BİST Teknoloji (XUTEK) endeksleri arasındaki ilişkilerin incelenmesi olarak belirlenmiştir. Bu amaçla çalışma kapsamında Borsa İstanbul 100 fiyat endeksi, Borsa İstanbul Mali fiyat endeksi ve Borsa İstanbul Teknoloji fiyat endeks ile Bitcoin arasındaki kısa ve uzun dönemli ilişki ARDL sınır testi yaklaşımı ile incelenmiştir. Elde edilen bulgular, Bitcoin fiyatı ile Borsa İstanbul Mali Endeksi arasında uzun dönem bir ilişkinin var olduğunu göstermiştir. Ancak, Bitcoin fiyatı ile diğer endeks fiyatları arasında uzun dönemli bir ilişkinin varlığına yönelik herhangi bir bulguya ulaşılamamıştır. Elde edilen kısa dönem bulgular ise Bitcoin fiyatı ile Borsa İstanbul Mali fiyat endeksi arasında herhangi anlamlı bir ilişkinin bulunmadığını göstermektedir.
Bu makalenin amacı kriptopara birimleri olarak da adlandırılan merkezi olmayan para birimleri olan Bitcoin Cash, Ethereum, Litecoin ve Ripple arasındaki ilişkilerin ortaya çıkarılmasıdır. Çalışmada üzerinde çalışılan dönem 03.08.2017 – 17.03.2020 tarihleri arasıdır. Çalışmada birim kök testi olarak Augmented Dickey-Fuller (ADF) testi uygulanarak serilerin durağan olduğu düzeyler saptanmış ve aralarındaki nedensellik ilişkisi Granger nedensellik testi ile sınanmıştır. Seriler arasındaki ilişkilerin yönü ve büyüklüğü, vektör otoregresif (VAR) model tekniğiyle belirlenmeye çalışılmıştır. Ayrıca, etki-tepki analizleri ve varyans ayrıştırma analizleri yapılarak serilerin standart sapmasında meydana gelen değişimin dönem bazında % kaçının diğer değişkenler tarafından açıklandığı ortaya konmuştur.
Amaç -Bu araştırmanın amacı, kripto para piyasasında en büyük kapitalizasyona ve en çok işlem hacmine sahip kripto para olan Bitcoin'in, volatilitesini en
John Maynard Keynes proposed the concept of ‘Bancor’ in 1940 as a supranational currency that would serve as the international reserve currency. The concept did not take off at the time, despite the underlying need to liberate the international system from the hegemonic tendencies of a national currency serving as a global medium of exchange. The emergence of Bitcoin makes it possible to revive the idea of a de-nationalised global medium of exchange. This article examines the feasibility of such an idea by examining a viable state policy for adoption and use in the international realm.
Aleksandra Polyakova, Zavyalov Dmitry, Vladimir Kolmakov
We investigate the interrelation between the economic policy uncertainty index and composite cryptocurrency index to contribute to the contemporary discussion and verify the available results of other researchers. Our research objective is to veri
There is a growing interest in the activities of the crypto market by various stakeholders. These stakeholders generally include investors, entrepreneurs, governments, fund managers, climate activists, institutional managers, employees with surplus funds, and crypto miners. This study aims to investigate the accuracy of the GARCH models for measuring and estimating Value-at-risk (VaR) using the Cryptocurrency index for future investment and managerial decision making. Because of this, the present study uses the top 30 Cryptocurrencies index in terms of Market capitalization excluding stable coins to determine the best GARCH models. Many entrepreneurs, institutional managers, fund managers, and other stakeholders have recently included cryptocurrency in their investment portfolio because of the increase in transactions and high returns growth in the global financial market with its associated high returns and volatility. Information communication technology has paved the way for such activities in the global markets. The daily data frequency was applied because of the availability of the data. The empirical analysis has been carried out for the period from January 2017 to December 2020 for a total of 1461observation. The returns volatility is estimated using SGARCH and EGARCH models. The findings evidenced that, using both normal distribution and Student t distribution, EGARCH provides a better measure and estimate than SGARCH concerning high persistence and volatility. Against this background, the present study also examined Backtesting to estimate Value at Risk. Interestingly, the findings of the available study would provide industry players, practitioners, entrepreneurs, and investors the maximum edge on how to use or measure such variables against others to make investment decisions. Also, the findings would subsequently contribute more insight into academia on the study area.
Two distinct and non-redundant understandings of volatility, as deviation from consistency, exist for a time-series: (1) exhibiting high standard deviation and, closer to the dictionary definition of the term, (2) appearing highly irregular and unpredictable. We find that Bitcoin is a prime example of an asset for which the two concepts of volatility diverge. We show that, historically, Bitcoin combines high Standard Deviation and low Approximate Entropy, relative to Gold and S&P 500. Moreover, subsample analysis for different time-scales (daily, weekly, monthly) shows that lower sampling frequencies drastically reduce the Kurtosis of the distribution of log-returns of Bitcoin. The opposite effect is observed for Gold and S&P 500. These properties suggest that, contrary to the volatility of the two traditional assets, Bitcoin’s high volatility is essentially an intra-day phenomenon that is strongly attenuated for a weekly or monthly time-preference.
Purpose The purpose of the paper is to better measure the risks and volatility of the Bitcoin market by using the proposed novel risk measurement model. Design/methodology/approach The joint regression analysis of value at risk (VaR) and expected shortfall (ES) can effectively overcome the non-elicitability problem of ES to better measure the risks and volatility of financial markets. And because of the incomparable advantages of the long- and short-term memory (LSTM) model in processing non-linear time series, the paper embeds LSTM into the joint regression combined forecasting framework of VaR and ES, constructs a joint regression combined forecasting model based on LSTM for jointly measuring VaR and ES, i.e. the LSTM-joint-combined (LSTM-J-C) model, and uses it to investigate the risks of the Bitcoin market. Findings Empirical results show that the proposed LSTM-J-C model can improve forecasting performance of VaR and ES in the Bitcoin market more effectively compared with the historical simulation, the GARCH model and the joint regression combined forecasting model. Social implications The proposed LSTM-J-C model can provide theoretical support and practical guidance to cryptocurrency market investors, policy makers and regulatory agencies for measuring and controlling cryptocurrency market risks. Originality/value A novel risk measurement model, namely LSTM-J-C model, is proposed to jointly estimate VaR and ES of Bitcoin. On the other hand, the proposed LSTM-J-C model provides risk managers more accurate forecasts of volatility in the Bitcoin market.
This study examined how the relationships among the fossil fuel, clean energy stock, gold, and Bitcoin markets have changed since the COVID-19 pandemic took place for hedging the price change risks in the fossil fuel markets. We applied the Bayesian Dynamic Conditional Correlation-Multivariate GARCH (DCC-MGARCH) models using US daily data from 2 January 2019 to 26 February 2021. Our results suggest that the fossil fuel (WTI crude oil and natural gas) and financial markets (clean energy stock, gold, and Bitcoin) generally had negative relationships in 2019 before the pandemic prevailed, but they became positive for a while in mid-2020, alternating between positive (0.8) and negative values (−0.8). As it is known that negative relationships are required among assets to hedge the risk of price changes, this implies that stakeholders need to be cautious in hedging the risk across the fossil fuel and financial markets when a crisis like COVID-19 occurs. However, our study also revealed that such negative relationships only lasted for three to six months, suggesting that the effects of the pandemic were short term and that stakeholders in the fossil fuel markets could cross hedge with the financial markets in the long term.
Purpose When investors experience extreme uncertainty, they seek “safe havens” to reduce their risk, to limit their losses and to protect the value of their portfolios. The purpose of this paper is to examine the safe-haven properties of Bitcoin compared to the stock market. Design/methodology/approach Based on intraday data, this study compares the price efficiencies of Bitcoin and Morgan Stanley Capital Index (MSCI) using Multifractal Detrended Fluctuation Analysis for the second half of 2020. This study then evaluates Bitcoin’s safe-haven property using Detrended Partial-Cross-Correlation Analysis (DPCCA). Findings This study finds that the price efficiency of Bitcoin is lower than that of MSCI. Further, Bitcoin was not a safe haven at any time for the MSCI index. The net cross-correlations between Bitcoin and MSCI are weak and they vary at different time scales. Research limitations/implications The behavior of market prices varies over time. Therefore, it is important to replicate this study for other time periods. Social implications The paper sheds light on the price behavior of Bitcoin during a period of instability. The results suggest that the construction of portfolios should differ based on the time horizons of the investors. Originality/value The authors compare Bitcoin against a global equity index instead of a specific country index or commodity. They also demonstrate the applicability of DPCCA in finance research.
While stablecoins such as Tether closely track the peg, there is some evidence for recurring spikes in stablecoins’ intraday volatilities rendering stablecoin volatilities unstable (Grobys et al., 2021). Using the Barndorff-Nielsen and Shephard (2006a) methodology, the purpose of our study is to examine whether jumps in Tether have an impact on (subsequent) Bitcoin returns. We retrieve hourly data for Bitcoin and Tether from Bitfinex covering the November 2018 to June 2021 period and encode the binary choice (1 – ‘jump’ and 0 – ‘no jump’) using bi-power variation based on asymptotic distribution theory at 5% significance level for each trading day. Our results show that the joint effect of positive jumps in Tether in association with an 1% increase in Tether returns on the prior day significantly predict negative prices changes in Bitcoin ranging from -3.65% to -8.49% in daily terms. Our results remain robust even after controlling for various other variables.