The current study investigates the connectedness between US COVID-19 news, Dowes Jones Index (DJI), green bonds, gold, and bitcoin prices for the period 22 January 2020–3 August 2021. The study has employed wavelet coherency, the continuous wavelet transform, and the wavelet-based Granger causality methods to obtain the dependence result. The continuous wavelet transform (CWT) analysis reveals that the United States equity market prices are extremely sensitive with regard to spreading coronavirus (USCOVID-19) news and changes in the oil price. Green bonds, gold, and bitcoin have minimal connectedness with the equity market, which might lead to the hedge and safe haven role of these assets during the COVID-19 crisis period. Lastly, very strong comovement was found between bitcoin and gold during the entire sample. The results of the present study offer a number of fresh and noticeable policy implications for international investors and asset managers.
Çin, ilk SARS-CoV-2 (COVID-19) vakasını 31.12.2019 tarihinde Dünya Sağlık Örgütü'ne (DSÖ) bildirmiştir. Bununla birlikte söz konusu virüs kısa sürede Dünya'da 200'den fazla ülkeye yayılmıştır. 07.04.2021 tarihi itibariyle Dünyada 133 milyondan fazla vaka tespit edilirken, yaklaşık 2.9 milyon kişi hayatını kaybetmiştir. Pandemi koşullarında hükümetler vatandaşlarını korumak için farklı politikalar izlediler. Pandemi, bu ülkelerdeki sağlık sistemine ek olarak borsaları ve altın, petrol, kripto para gibi çeşitli küresel varlıkları da etkilemiştir. Bu çalışmada, 26.06.2018 - 07.04.2021 dönemi için piyasa değeri en yüksek olan kripto para birimlerinde fiyat balonlarının varlığının incelenmesi amaçlanmaktadır. Bu çalışma kapsamında, pandemi öncesi ve pandemi döneminde on kripto para birimindeki fiyat balonlarının araştırılması için SADF testi uygulanmıştır. Buna göre, on kripto para biriminden sekizinin fiyat balonuna sahip olduğu tespit edilmiştir. Pandemi öncesi dönemde en yüksek fiyat balonu sayısının toplam 84 işlem günü ile Binance Coin'de görüldüğü belirlenmiştir. Sırasıyla, Bitcoin, Chainlink (Bitcoin ile aynı gün sayısı), Litecoin ve Tether bu kripto para birimini takip etmiştir. Bununla birlikte, COVID-19 salgınında işlem günü bazında en yüksek fiyat balonu, toplam 230 gün ile Theta'da görülmüştür. Pandemi döneminde, Chainlink, Bitcoin, Ethereum, Cordano, Binance Coin ve Litecoin, Theta'yı sırasıyla 183, 140, 104, 94, 66 ve 28 işlem günü ile takip etmiştir. Fiyat balonlarının yaklaşık yüzde yetmiş beşi pandemi döneminde görülmüştür. Bu sonuçlar, kripto para birimlerinin yeni yatırımlar için spekülatif varlıklar olduğunu göstermektedir. Tüm analiz dönemi değerlendirildiğinde ise toplam 234 gün ile Chainlink'te en yüksek fiyat balonu tespit edilmiştir. Ayrıca, Bitcoin 131 gün ile aralıksız en uzun fiyat balonunu göstermiş, Theta ve Ethereum bu kripto parayı takip etmiştir.
David Opeoluwa Oyewola, Emmanuel Gbenga Dada, Juliana Ngozi Ndunagu, Daniel Eneojo Emmanuel
In the wake of recent pandemic of COVID-19, we explore its unprecedented impact on the demand and supply of cryptocurrencies’market using machine learning such as Naïve Bayes (NB), Decision Trees (C5), Decision Trees Bagging (BG), Support Vector Machine (SVM), Random Forest (RF), Multinomial Logistic Regression (MLR), Recurrent Neural Network (RNN), Long Short Term Memory and Noise Bagging (NBG). The study employed Noise filters to enhance the performance of Decision Trees Bagging named NBG. Dataset utilized for this analysis were obtained from the website of Coin Market Cap, including: Binance Coin (BCN), BitCoin Cash (BCH), BitCoin (BTC), BitCoinSV (BSV), Cardano (CDO), Chainlink (CLK), CryptoCoin (CCN), EOS (EOS), Ethereum (ETH), LiteCoin (LTC), Monero (MNO), Stellar (SLR), Tether (TTR), Tezos (TZS), XRP (XRP), and daily data collected from exchange markets platforms spans from 2nd January 2018 to 7th July 2020. Auto encoder was utilized for the labelling of the trading strategies buy-hold-sell.
The blockchain technology introduced by bitcoin, with its decentralised peer-to-peer network and cryptographic protocols, provides a public and accessible database of bitcoin transactions that have attracted interest from both economics and network science as an example of a complex evolving monetary network. Despite the known cryptographic guarantees present in the blockchain, there exists significant evidence of inconsistencies and suspicious behavior in the chain. In this paper, we examine the prevalence and evolution of two types of anomalies occurring in coinbase transactions in blockchain mining, which we reported on in earlier research. We further develop our techniques for investigating the impact of these anomalies on the blockchain transaction network, by building networks induced by anomalous coinbase transactions at regular intervals and calculating a range of network measures, including degree correlation and assortativity, as well as inequality in terms of wealth and anomaly ratio using the Gini coefficient. We obtain time series of network measures calculated over the full transaction network and three sub-networks. Inspecting trends in these time series allows us to identify a period in time with particularly strange transaction behavior. We then perform a frequency analysis of this time period to reveal several blocks of highly anomalous transactions. Our technique represents a novel way of using network science to detect and investigate cryptographic anomalies.
Khreshna Syuhada, Djoko Suprijanto, Arief Rachman Hakim
This paper aims to compare the safe-haven roles of gold and Bitcoin for energy commodities, including oils and petroleum, during COVID-19. Specifically, we examine the presence of reduction in downside risk after mixing gold/Bitcoin with such energy commodities. To do this, we account for dependence among energy commodities and gold/Bitcoin returns by applying a (vine) copula. The findings show that gold substantially reduces the downside risk of a portfolio containing any allocation to gold and energy commodities, indicating its safe-haven ability. In contrast, Bitcoin's safe-haven functionality is inconsistent since the downside risk reduction is achieved for Bitcoin's small allocation only.
Bu çalışmada, Bitcoin elektrik tüketiminin, Bitcoin üretiminde önde gelen seçili ülkelerin enerji piyasaları ile arasındaki ilişki araştırılmıştır. Bu amaç doğrultusunda 22.05.2017-10.02.2021 dönemleri arasında haftalık veriler kullanılarak; Cambridge Bitcoin Elektrik Tüketim Endeksi (CBECI) ile S&P 500, MOEX ve SSE enerji endeksleri arasındaki volatilite hareketleri incelenmiştir. CCC-GARCH modeliyle kurgulanan analizlerden elde edilen bulgular CBECI endeksinin; MOEX enerji endeksi ile arasında çift yönlü volatilite ilişkisi olduğunu, S&P 500 ve SSE enerji endeksleri ile arasında tek yönlü bir volatilite ilişkisi olduğunu göstermektedir. Bulgular Bitcoin elektrik tüketiminin, Rusya ve Çin’in enerji şirketi değerlemelerini etkilediği; ABD ve Rusya’nın enerji şirketi değerlemelerinden etkilendiği sonucuna ulaşılmaktadır.
In December 2017, two leading derivative exchanges, CBOE and CME, introduced the first regulated Bitcoin futures. Our aim is estimating their causal impact on Bitcoin volatility and trading volume. Employing a new causal approach, C-ARIMA, we find that the CME future triggered an increase in both outcomes. There is also evidence of a positive volume-volatility relationship and that the effect on volatility was partially due to the higher trading volumes induced by the launch of the contract. After controlling for the effect on volumes, we find that the CME instrument caused Bitcoin volatility to increase by more than double.
Bu çalışmada popülerliği son yıllarda artan kripto paralar sınıfında değerlendirilen Bitcoin ile Euro getirileri arasındaki volatilite etkileşimini incelemek için 02.02.2014-28.02.2021 dönemine ait günlük veriler kullanılmıştır. Değişkenlere ait getirilerin zaman içindeki hareketini incelemek için oluşturulan grafiklerden volatilite kümelenmesi tespit edilmiş ve çok değişkenli GARCH modelleri kullanılmıştır. Modellerden elde edilen sonuçlar karşılaştırılarak log-olabilirlik değeri en küçük(negatif olarak) bulunan BEKK-GARCH modeli uygun model kabul edilerek Euro ile Bitcoin arasında çift yönlü volatilite etkileşimi bulunmuştur. Ayrıca DCC-GARCH modeli sonuçlarına göre ise iki getiri arasında asimetri ilişkisi ve oranında pozitif, güçlü bir dinamik korelasyon tespit edilmiştir.
Non-linear interactions between cryptocurrency price movements can elicit cross-frequency coupling (CFC) wherein one set of frequencies in the 1st timeseries is coupled to another set of frequencies in the 2nd timeseries. To investigate this, we use a generalized coherence approach to detect and quantify both linear (i.e., iso-frequency coupling, IFC) and non-linear coherence (CFC) and the associated phase relationships between the intra-day price changes of various pairs of cryptocurrencies for the year 2020. Using this information, we further assess the risk reduction associated with diversification of portfolios between each pair of a small market capital and a large market capital cryptocurrency, for both synchronous and asynchronous trading conditions. While mean pairwise IFC values were lower for smaller cryptocurrencies, pairwise CFC values were more heterogeneous and had no correlation with the market capital size. Diversification of portfolios resulted in reduced risk for synchronously-traded pairs of those cryptocurrencies which had low IFC. For asynchronous trading conditions, if the larger market capital cryptocurrency was traded at a higher frequency, diversification almost always reduced risk. Thus, the novel approach used in this study reveals important insights into the complex dynamics that govern the price trends of cryptocurrencies.
Esam Mahdi, Víctor Leiva, Saed Mara’Beh, Carlos Martín-Barreiro
In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic.
The recent 50% drop in the price of the flagship cryptocurrency Bitcoin reinforces the persistent anxiety among cryptocurrency investors. Can alternative assets hedge Bitcoin risk? This study investigates the ability of equities, commodities, bonds, currencies, and VIX futures to hedge Bitcoin. Our in-sample analysis shows that the USDX, Gilt, Australian dollars, wheat, cocoa, cotton, sugar, copper, and lean hog can hedge Bitcoin, and the out-of-sample analysis reveals that the DAX, Dow-Jones, Nikkei, S&P 500, Brent, and WTI futures can be effective hedging instruments. We use a wavelet-based dynamic hedging model to account for heterogeneous investors in the Bitcoin market. For a short-term horizon, soybean futures reduce the variance in the in-sample hedged portfolio, and cotton futures offer the highest out-of-sample utility. Copper futures are the best for in-sample hedging in a long-term horizon, whereas live cattle futures have the best out-of-sample performance. These results show that conventional assets can hedge wild swings in Bitcoin.
The purpose of this article has been the Bitcoin rates modeling, as the most important digital currency, by depending on 1932 daily observations. As a result , the Bitcoin rates follow the ARIMA(1,1,2) model while the residuals pursue GARCH(1.1) model . In the second semester of 2017, a structural change was noticed, at that moment, the Bitcoin has reached the highest level, and overcame the rate of 16560 Euro. The Bitcoin leap is due to several factors, the most important ones are that it has been accredited as a legal currency by many great world governments , benefits of the tax exemption for its users , has been considered as an entertainment tool , and a short term hedging tool as many researchers have declared .
French title: Modelisation de pieces Bitcoin utilisant le modele autoregressif heteroscedasticite conditionnelle
Cet article vise a modeliser les valeurs de bitcoin comme la monnaie numerique la plus importante a travers les vues quotidiennes de 1932. Il a ete constate que les valeurs de bitcoin suivent le modele ARIMA (1,1,2) tandis que les autres suivent le modele GARCH(1,1), en plus de surveiller les changements structurels dans la serie au deuxieme semestre 2017, au cours de cette periode, le bitcoin a atteint un record, depassant 16590 euros. Le boom du bitcoin est du a plusieurs facteurs, dont le plus important est son acceptation dans de nombreux grands pays comme monnaie legale, l'exoneration fiscale de son detenteur, en plus d'etre consideree comme une methode de luxe, en particulier avec ses avantages, car de nombreux chercheurs ont souligne qu'il s'agissait d'un outil de couverture a court terme.
In recent years, there has been an increase in demand for machine learning and AI-assisted trading. To extract abnormal profits from the bitcoin market, the machine learning and artificial intelligence (AI) assisted trading process has been used. Each day, the data gets saved for the specified amount of time. These approaches produce great results when integrated with cutting-edge algorithms. The results of algorithms and architectural structures drive the development of cryptocurrency market. The unprecedented increase in market capitalization has enabled the cryptocurrency to flourish in 2017. Currently, the market accommodates totally 1500 cryptocurrencies, all of which are actively trading. It is always possible to mine the cryptocurrency and use it to pay for online purchases. The proposed research study is more focused on leveraging the accurate forecast of bitcoin prices via the normalization of a particular dataset. With the use of LSTM machine learning, this dataset has been trained to deploy a more accurate forecast of the bitcoin price. Furthermore, this research work has evaluated different machine learning methods and found that the suggested work delivers better results. Based on the resultant findings, the accuracy, recall, precision, and sensitivity of the test has been calculated.
Klaus Grobys, Juha-Pekka Junttila, James W. Kolari, Niranjan Sapkota
This paper investigates the volatility processes of stablecoins and their potential stochastic interdependencies with Bitcoin volatility. We employ a novel approach to choose the optimal combination for the power law exponent and the minimum value for the volatilities bending the power law. Our results indicate that Bitcoin volatility is well-behaved in a statistical sense with a finite theoretical variance. Surprisingly, the volatilities of stablecoins are statistically unstable and contemporaneously respond to Bitcoin volatility. Also, whereas the volatilities of stablecoins are not Granger-causal for Bitcoin volatility, lagged Bitcoin volatility exhibits Granger-causal effects on the volatilities of stablecoins. We conclude that Bitcoin volatility is a fundamental factor that drives the volatilities of stablecoins.
Do behavioral factors mediate the relationship between industry returns and Bitcoin returns? We use four industry indices in technology, energy, clean energy, and banking, and the Sentiment index from Thomson Reuters Marketpsych Indices as a behavioral factor to investigate this question. We show that the sensitivities of technology and clean energy industry indices to Sentiment, positively and significantly, strengthen the relationship between sentiment and Bitcoin returns. By showing that behavioral factors mediate the association between the returns of industry indices and Bitcoin returns, we provide evidence that investors’ Sentiment captures the association between Bitcoin and sectors related to cryptocurrencies. Our results, however, do not support prior studies’ findings of a direct relationship between the industry indices and Bitcoin returns.
The development of the green bond market has been magnificent recently, but it is necessary to be accelerated for financial sustainability over the globe. In response to increasing interest in the time-varying nexus between green bonds and other assets, the current study empirically investigates the asymmetric relationship between green bonds and other conventional assets, including Bitcoin price, S&P 500, Clean Energy Index, GSCI Commodity Index, and CBOE volatility using recently proposed and novel methods of quantile on quantile regression and Granger causality in quantiles approaches. Our mainstream results demonstrate that other assets under study strengthen green bonds over sample period studied, and this impact is more pronounced in higher quantiles of respective variables. Moreover, our quantile causality test further confirms these results with robust finding across time scales and quantiles. To enhance clean energy and energy efficiency, policymakers should take into consideration limiting eligibility criteria in policies supporting green bonds or limiting refinancing using green bonds. Stakeholders driving the green bond market should scale up the market to finance the required global investment level.
Dimitrios Koutmos, Timothy King, Constantin Zopounidis
Abstract Are cryptocurrencies useful minimum‐variance hedging instruments? This paper develops a two‐step analytical framework to explore this question across time. First, it estimates dynamic optimal weights, calibrated when investing between the aggregate market and a respective sampled cryptocurrency. This is performed separately for 11 major cryptocurrencies using the dynamic conditional correlation approach of Engle. Second, using a fractional regression approach, it uncovers linkages between optimal weights in cryptocurrencies and sources of economic uncertainty. Overall, this paper makes the following important findings. First, optimal weights in cryptocurrencies all rose rapidly during the COVID‐19 pandemic. In all, bitcoin showed to be the leading cryptocurrency in terms of hedging effectiveness during this recent time period. Second, most cryptocurrencies exhibit zero or negative betas consistently across time, thus making them natural hedging instruments for investors seeking to reduce their portfolio's comovement with the market. Finally, cryptocurrencies serve as better hedges for economic uncertainties arising from equity and commodity markets. They are relatively less effective for uncertainties arising from risks in the banking industry and firm default risk. This paper contributes broadly to the asset pricing literature since our two‐step approach herein can tractably be extended to other asset classes or other econometric measures of systematic risk.
Lykke Øverland Bergsli, Andrea Falk Lind, Péter Molnár, Michał Polasik
Since Bitcoin price is highly volatile, forecasting its volatility is crucial for many applications, such as risk management or hedging. We study which model is the most suitable for forecasting Bitcoin volatility. We consider several GARCH and two heterogeneous autoregressive (HAR) models and compare them. Since we utilize realized variance estimated from high frequency data as a proxy for true volatility, we can draw sharper conclusions than studies which use only daily data. We find that EGARCH and APARCH perform best among the GARCH models. HAR models based on realized variance perform better than GARCH models based on daily data. Superiority of HAR models over GARCH models is strongest for short-term volatility forecasts.
Many traders believe in and use Twitter tweets to guide their daily cryptocurrency trading. In this project, we investigated the feasibility of automated sentiment analysis for cryptocurrencies. For the study, we targeted one cryptocurrency (NEO) altcoin and collected related data. The data collection and cleaning were essential components of the study. First, the last five years of daily tweets with NEO hashtags were obtained from Twitter. The collected tweets were then filtered to contain or mention only NEO. We manually tagged a subset of the tweets with positive, negative, and neutral sentiment labels. We trained and tested a Random Forest classifier on the labeled data where the test set accuracy reached 77%. In the second phase of the study, we investigated whether the daily sentiment of the tweets was correlated with the NEO price. We found positive correlations between the number of tweets and the daily prices, and between the prices of different crypto coins. We share the data publicly.
We investigate any similarity and dependence based on the full distributions of cryptocurrency assets, stock indices and industry groups. We characterize full distributions with entropies to account for higher moments and non-Gaussianity of returns. Divergence and distance between distributions are measured by metric entropies, and are rigorously tested for statistical significance. We assess the stationarity and normality of assets, as well as the basic statistics of cryptocurrencies and traditional asset indices, before and after the COVID-19 pandemic outbreak. These assessments are not subjected to possible misspecifications of conditional time series models which are also examined for their own interests. We find that the NASDAQ daily return has the most similar density and co-dependence with Bitcoin daily return, generally, but after the COVID-19 outbreak in early 2020, even S&P500 daily return distribution is statistically closely dependent on, and indifferent from Bitcoin daily return. All asset distances have declined by 75% or more after the COVID-19 outbreak. We also find that the highest similarity before the COVID-19 outbreak is between Bitcoin and Coal, Steel and Mining industries, and after the COVID-19 outbreak is between Bitcoin and Business Supplies, Utilities, Tobacco Products and Restaurants, Hotels, Motels industries, compared to several others. This study shed light on examining distribution similarity and co-dependence between cryptocurrencies and other asset classes.
Walid Mensi, Mobeen Ur Rehman, Muhammad Shafiullah, Khamis Hamed Al‐Yahyaee · 5 authors
This paper examines the high frequency multiscale relationships and nonlinear multiscale causality between Bitcoin, Ethereum, Monero, Dash, Ripple, and Litecoin. We apply nonlinear Granger causality and rolling window wavelet correlation (RWCC) to 15 min-data. Empirical RWCC results indicate mostly positive co-movements and long-term memory between the cryptocurrencies, especially between Bitcoin, Ethereum, and Monero. The nonlinear Granger causality tests reveal dual causation between most of the cryptocurrency pairs. We advance evidence to improve portfolio risk assessment, and hedging strategies.
Understanding the dependence and risk spillover among hedging assets is crucial for portfolio allocation and regulatory decision making. Using various copula and conditional Value-at-Risk (CoVaR) measures, this paper quantifies the dependence and risk spillover effects between three traditional and emerging hedging assets: Bitcoin, gold, and USD. Furthermore, we investigate these effects at various short- and long-term horizons using a variational model decomposition (VMD) method. The empirical results show that there is strong negative dependence between gold and USD, but Bitcoin and gold are weakly and positively connected. Secondly, risk spillovers exist only between Bitcoin and gold and between gold and USD. The risk spillover effect between Bitcoin and gold are not stable, that is, if Bitcoin or gold faces the downward or upward risk, both the downward and upward risk of another asset have the chance to increase. The negative risk spillover between gold and USD is stable, especially in long-term horizons. Finally, the risk spillover between Bitcoin and gold as well as between gold and USD are asymmetric at downward and upward market environment.
The high power consumption of Bitcoin transactions has raised environmental and sustainable concerns of green investors and regulatory bodies. We utilize the time-varying optimal copula (TVOC) approach to showcase the dependence structure between bitcoin and green financial assets. We find multiple tail-dependence regimes characterize the extreme dependence between bitcoin and green financial assets, and the dependence structure is mainly asymmetric and time-varying. Finally, the hedging effectiveness of green financial assets for bitcoin revealed that all green assets, especially clean energy, are effective hedges for bitcoin.
Due to the transparency, simplicity, and blockchain system, cryptocurrencies gained popularity in the modern world. This led to more use of cryptocurrencies for speculation and investment rather than a medium of exchange. It is crucial to analyse the nature of the crypto market before investing in such currencies. With this intention, the paper tried to know the extent of following (Followness) of altcoins to the bitcoin in the different dominance phases like High Dominance, Low Dominance, and Moderate Dominance. For this purpose, daily closing prices of the Bitcoin and five major altcoins (Ethereum, Litecoin, Namecoin, Doge, and Ripple) are collected for the last five years and analyse the relationship between bitcoin and altcoins. Pearson's correlation coefficient test is used to know the direction of the relationship, and Vector Error Correction Model is used to see the extent of the relation. In general, the empirical result of the study showed cointegration between bitcoin and Altcoin. It also depicted that Altcoin showed a high level of followness in the moderate dominance phase and low followness in the low dominance phase. The study developed a price estimation equation to predict the price of altcoins depending upon the price of bitcoin and its dominance in the crypto market. This paper concludes that the dominance of Bitcoin also has a significant role in the price movement of altcoins.