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.
By using high-frequency data, we examine the volatility linkages patterns between gold and several important asset classes including foreign currency, US equity, oil, bitcoin and agriculture commodity in the period surrounding the COVID-19 pandemic. To this end, we use the cross-wavelet power transform, the cross-wavelet coherency and the dynamic frequency-domain connectedness. We find that the pandemic caused a greater positive association in volatility series between gold and each of the financial assets considered. We document clear findings of phase difference of lead-lag volatility interdependence between gold and the financial assets that varies according to timescales and periods. In general, the long-term connections are strengthened during the pandemic except the case of bitcoin and soya bean suggesting a long-term diversification ability when including them in a portfolio containing gold.
Purpose This paper aims to investigate whether Islamic indexes, Bitcoin and gold still act as hedges or/and “safe-haven” assets during the COVID-19 pandemic crisis. This paper examines the role of the Morgan Stanley Capital International all-country world index, Islamic index, gold and Bitcoin as a hedge or safe-haven asset for the world conventional stock market over the period from April 30, 2015 to March 27, 2020. Design/methodology/approach In this paper, the authors re-evaluate the hedge and safe haven properties of Islamic indexes, gold and Bitcoin following Baur and Lucey’s (2010) and Baur and McDermott’s (2010) methodology. Findings Empirical results show that the Islamic index is not a hedge or a safe haven asset for the world conventional stock market during the recent coronavirus crisis period. Different from the whole period, the authors find that gold is a strong hedge but only a weak safe or is not a safe haven during the coronavirus sub-period. Bitcoin reports distinctive properties, as it acts as a weak hedge and not a safe-haven asset. Originality/value This paper is the first study that investigates whether the global Islamic index still acts as hedges or “safe-haven” assets during the new COVID-19 crisis period. The results can help investors make informed decisions when adding cryptocurrencies and Islamic indexes to their portfolios during the coronavirus crisis.
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.
Ali Yavuz Polat, Ahmet Faruk Aysan, Hasan Tekin, Ahmet Semih Tunalı
Purpose This study aims to investigate the effect of fear sentiment with a novel data set on Bitcoin’s (BTC) return, volatility and transaction volume. The authors divide the sample into two subperiods to capture the changing dynamics during the COVID-19 pandemic. Design/methodology/approach The authors retrieve the novel fear sentiment data from Thomson Reuters MarketPsych Indices (TRMI). The authors denote the subperiods as pre- and post-COVID-19 considering January 13, 2020, when the first COVID-19 confirmed case was reported outside China. The authors use bivariate vector autoregressive models given below with lag-length k, to investigate the dynamics between BTC variables and fear sentiment. Findings BTC market measures have dissimilar dynamics before and after the Coronavirus outbreak. The results reveal that due to the excessive uncertainty led by the outbreak, an increase in fear sentiment negatively affects the BTC returns more persistently and significantly. For the post-COVID-19 period, an increase in fear also results in more fluctuations in transaction volume while its initial and cumulative effects are both negative. Due to extreme uncertainty caused by the COVID-19 pandemic, investors may trade more aggressively in the initial phases of the shock. Practical implications The authors are convinced that the results in this paper have more far-reaching implications for other markets regulated by the states. BTC provides a natural benchmark to understand how fear sentiment drives and impacts the markets isolated from any interventions. Hence, the results show that in the absence of regulatory frameworks, market dynamics are likely to be more volatile and the fear sentiment has more persistent impacts. The authors also highlight the importance of using micro, asset-specific sentiment measures to capture market dynamics better. Originality/value BTC is not associated with any regulatory authority and is not produced by the governments and central banks. COVID-19 as a natural experiment provides an opportunity to explore the pure effects of market sentiment on BTC considering its decentralized and unregulated features. The paper has two main contributions. First, the authors use BTC-specific fear sentiment novel data set of TRMI instead of more general market sentiments used in the existing studies. Next, this is the first study to examine the association between fear and BTC before and after COVID-19.
Purpose This study aims to investigate the diversification benefits attached to the crypto portfolios when combined with stocks, Forex instruments and commodity assets. Design/methodology/approach Markowitz diversification techniques have been used to analyze the risk-return tradeoffs of the individual portfolios. Daily prices on cryptocurrencies and the selected asset classes, cover the period before and during the pandemic COVID-19. The portfolio risk of the portfolios was calculated by identical techniques and analyzed with equal criteria. Findings The results with 270 trails indicate that stocks on average reduce the portfolio risk of crypto portfolios by 36% followed by fiat currency with 30.9% and commodities by 20.8%. Average daily returns stand in line with the standard portfolio theories where riskier portfolios offer higher returns and the other way around. Originality/value The authors contribute to the current literature by investigating the portfolio risk attached to the crypto portfolios when stocks, commodities and Forex instruments were added separately. To this end, results inform not only retail investors but also portfolio managers on the asset classes that generate better optimization for crypto portfolios.
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.
Mahdi Ghaemi Asl, Muhammad Mahdi Rashidi, Seyed Ali Hosseini Ebrahim Abad
Purpose The purpose of this study is to investigate the correlation between the price return of leading cryptocurrencies, including Bitcoin, Ethereum, Ripple, Litecoin, Monero, Stellar, Peercoin and Dash, and stock return of technology companies' indices that mainly operate on the blockchain platform and provide financial services, including alternative finance, democratized banking, future payments and digital communities. Design/methodology/approach This study employs a Bayesian asymmetric dynamic conditional correlation multivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (BADCC-MGARCH) model with skewness and heavy tails on daily sample ranging from August 11, 2015, to February 10, 2020, to investigate the dynamic correlation between price return of several cryptocurrencies and stock return of the technology companies' indices that mainly operate on the blockchain platform. Data are collected from multiple sources. For parameter estimation and model comparison, the Markov chain Monte Carlo (MCMC) algorithm is employed. Besides, based on the expected Akaike information criterion (EAIC), Bayesian information criterion (BIC), deviance information criterion (DIC) and weighted Deviance Information Criterion (wDIC), the skewed-multivariate Generalized Error Distribution (mvGED) is selected as an optimal distribution for errors. Finally, some other tests are carried out to check the robustness of the results. Findings The study results indicate that blockchain-based technology companies' indices' return and price return of cryptocurrencies are positively correlated for most of the sampling period. Besides, the return price of newly invented and more advanced cryptocurrencies with unique characteristics, including Monero, Ripple, Dash, Stellar and Peercoin, positively correlates with the return of stock indices of blockchain-based technology companies for more than 93% of sampling days. The results are also robust to various sensitivity analyses. Research limitations/implications The positive correlation between the price return of cryptocurrencies and the return of stock indices of blockchain-based technology companies can be due to the investors' sentiments toward blockchain technology as both cryptocurrencies and these companies are based on blockchain technology. It could also be due to the applicability of cryptocurrencies for these companies, as the price return of more advanced and capable cryptocurrencies with unique features has a positive correlation with the return of stock indices of blockchain-based technology companies for more days compared to the other cryptocurrencies, like Bitcoin, Litecoin and Ethereum, that may be regarded more as speculative assets. Practical implications The study results may show the positive role of cryptocurrencies in improving and developing technology companies that mainly operate on the blockchain platform and provide financial services and vice versa, suggesting that managers and regulators should pay more attention to the usefulness of cryptocurrencies and blockchains. This study also has important risk management and diversification implications for investors and companies investing in cryptocurrencies and these companies' stock. Besides, blockchain-based technology companies can add cryptocurrencies to their portfolio as hedgers or diversifiers based on their strategy. Originality/value This is the first study analyzing the connection between leading cryptocurrencies and technology companies that mainly operate on the blockchain platform and provide financial services by employing the Bayesian ssymmetric DCC-MGARCH model. The results also have important implications for investors, companies, regulators and researchers for future studies.
Do Cryptocurrencies fear Coronavirus? This paper answers this question by examining the predictive power of the Covid-19 global fear index of Salisu and Akanni (2020) on major cryptocurrencies' returns during the period from 07/02/2020 to 05/03/2021. First, we formulate a predictive model of major cryptocurrencies' returns based on the Covid-19 global fear index. Second, we combine the global fear of the pandemic with other fear proxies and we present a multiple-factor fear-based predictive model that captures the effects of other economic and financial fear variables. Finally, we examine whether accounting for asymmetries would improve the predictability of returns. The empirical findings show that the global fear index contains information that help predict major cryptocurrencies and that the multiple-factor model is a better predictive model for cryptocurrencies' returns. Specifically, global fear related to health risks exhibits a significantly negative impact on the majority of the sampled cryptocurrencies' returns. Consistent with in-sample results, global fear provides a statistically significant out-of-sample forecast outcome. Our results suggest that, in the period of the pandemic, cryptocurrencies are not very different from other assets and that they exhibit a significant reaction to the fear environment.
Mateus Portelinha, Carlos Heitor Campani, Raphael Moses Roquete
This study analyses the impact of including cryptocurrencies in Brazilian stocks' portfolios performances from September 2014 to April 2020. The comparisons were made between stocks' only portfolios against portfolios that allowed stocks and cryptocurrencies. Three portfolios served as benchmarks: the naïve but relevant equally weighted portfolio, the tangency and the MVP portfolios built from the Markowitz mean-variance theory. Performances were compared through out-of-sample returns, volatilities, Sharpe, Sortino and Omega ratios. Our results indicate positive statistically significant return and risk-adjusted improvements after the inclusion of cryptocurrencies, although also increasing the volatility. The equally weighted portfolios with cryptocurrencies often outperformed the tangency and minimum variance models, which only exhibited better results when more data was used as input to the models. Moreover, the portfolios that included cryptocurrencies consistently outperformed the IBrX-100 in the period studied. The results of this study are important for investors and fund managers, especially because cryptocurrencies are yet not considered by most of them.
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.
Bitcoin has received a lot of attention from investors, researchers, regulators, and the media. It is a known fact that the Bitcoin price usually fluctuates greatly. However, not enough scientific research has been done on these fluctuations. In this study, long short-term memory (LSTM) modeling from Recurrent Neural Networks, which is one of the deep learning methods, was applied on Bitcoin values. As a result of this application, anomaly detection was carried out in the values from the data set. With the LSTM network, a time-dependent representation of Bitcoin price can be captured, and anomalies can be selected. The factors that play a role in the formation of the model to be applied in the detection of anomalies with the experimental results were evaluated.
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.