Purpose This study investigates the diversification benefits of multiple cryptocurrencies and their usefulness as investment assets, individually or combined, in enhancing the performance of a well-diversified portfolio of traditional assets before and during the pandemic COVID-19. Design/methodology/approach This paper uses two optimization techniques, namely the mean-variance and the maximum Sharpe ratio. The naïve diversification rules are used for comparison. Besides, the Sharpe and the Sortino ratios are used as performance measures. Findings The results show that cryptocurrencies diversification benefits occur more during the COVID-19 pandemic rather than before it, with the maximum Sharpe ratio portfolio presenting its highest performance. Furthermore, the results suggest that, during COVID-19, the diversification benefits are slightly better when using a combination of cryptocurrencies to an already well-diversified portfolio of traditional assets rather than individual ones. This serves to improve the performance of the maximum Sharpe ratio portfolio, and to some extent, the naïve portfolio. Yet, cryptocurrencies, whether added individually or combined to a well-diversified portfolio of traditional assets, don't fit in the minimum variance portfolio. Besides, the efficient frontier during COVID-19 pandemic dominates the one before COVID-19 pandemic, giving the investor a better risk-return trade-off. Originality/value To the best of the author's knowledge, this is the first study that examines the diversification benefits of multiple cryptocurrencies both as individual investments and as additional asset classes, before and during COVID-19 pandemic. The paper covers all analyses performed separately in previous studies, which brings new evidence regarding the potential for cryptocurrencies in portfolio diversification under different portfolio strategies.
Rasoul Amirzadeh, Dhananjay Thiruvady, Asef Nazari, Mong Shan Ee
Abstract Cryptocurrencies have gained widespread attention, particularly in finance and investment sectors. Despite their growing popularity, cryptocurrencies can be a high-risk investment due to their price volatility. The inherent volatility in cryptocurrency prices, coupled with the effects of external global economic factors, makes predicting their price movements challenging. To address this challenge, we propose a dynamic Bayesian network (DBN)-based approach to uncover potential causal relationships among various features including social media data, traditional financial market factors, and technical indicators. This study focuses on six major cryptocurrencies, including Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether. The proposed model’s performance is compared to five baseline models of auto-regressive integrated moving average, support vector regression, long short-term memory, random forests, support vector machines, and a large language model. Results demonstrate that while DBN performance varies across cryptocurrencies, with some cryptocurrencies exhibiting higher predictive accuracy than others, the DBN significantly outperforms the baseline models.
Purpose The aim of this research is to explore multiscale hedging strategies among cryptocurrencies, commodities, and GCC stocks. Particularly, this is done by evaluating the connectedness among these asset classes covering a period with COVID-19 implications. Using the wavelet approach, the present study aims to recommend whether there exist different time horizon-based hedging abilities across the asset classes. Design/methodology/approach The approach used in this study is a multiscale decomposition of time series based on wavelets of daily prices of 13 asset classes. Since the wavelet analysis allows to decompose the time series into its frequency components at different time scales by a filtering process the study covered 1-day, 8-day, and 64-day time horizons to examine the hedging properties across those asset classes. Findings The results of this study show that hedging effectiveness differs among stock markets over time. In some cases, cryptocurrencies may keep their hedging properties across time while in others they switch from safe haven to hedge devices. In almost all cases, the three main cryptocurrencies showed diversifying properties as was observed by the multiscale correlation and hedge ratio estimations. In a competing sense, gold showed safe haven properties across time than cryptocurrencies except at an 8-day time scale where hedge ratios were low, positive and statistically different from zero that could be interpreted as a good hedge device in the medium term. Research limitations/implications Though this research has considered a set of thirteen asset classes, it was limited to a period in which most cryptocurrencies started trading for the first time which reduces the number of observations compared to Bitcoin prices and stable coins such as Ethereum, Ripple, and Bitcoin Cash. Also, the research was focused on the GCC stock markets which may have different results as compared to other regional markets of Asia or Latin America. A comparative analysis in future could be another area of research in future. Practical implications This study has some significant policy implications. The cryptocurrency market is severely affected by demand and risk shocks to crude oil prices during the COVID-19 period. From the investor's point of view, diversification benefits can be obtained by combining cryptocurrencies along with oil-related products during episodes of financial turmoil and COVID-19 pandemic. The GCC region is constantly endeavoring to adopt more scientific tools and mechanisms of investment, and therefore, this study's results will provide some useful directions to the government, policymakers, financial institutions, and investors. Originality/value The current study covers a big bunch of 13 assets spanning across financial and real assets. This is based on literature gap and hence, will be a significant addition to the existing literature. Moreover, the GCC region is emerging as a global investment hub and this study will provide investors dynamic hedging strategies across these asset classes.
Using the onset of the COVID 19 pandemic, this chapter examines the cryptocurrencies as safe haven investment for stocks of our time varying realization as to the economic chock centralized by the growing pandemic. Using daily data of COVID 19 measures and daily prices for 4 cryptocurrencies and 4 stocks assets for the whole year 2020, we apply both the VAR-DCC-GARCH and Wavelet Coherency models. New evidence of our chapter find that the Bitcoin and Etherum are highly correlated in the short and long horizon with the selected stocks. However for the case of the Litecoin and the XRP are correlated negatively with the stocks in the whole COVID19 period. We find evidence that, Bitcoin is strong safe haven asset for all the selected stocks during the COVID19 era, while the Litecoin is weak safe haven investment for all the stocks and the XRP is with lowest potential of safe haven investment for all the studied stocks. Within the study we are providing a diversification of hedging for the investors and policy makers suggesting that the cryptocurrencies acted as safe haven investment similar to the precious metals during historic crisis and as fiat money for any economic shocks might occurred. The abstract should summarize the contents of the paper in short terms, i.e. 150-250 words.
Nimish Prabhune, Aman Mahajan, Manu Priyam Mittal, Rishi Kumar
This article focuses on the returns of four major cryptocurrencies. It covers a complete study of the dynamics of cryptocurrencies with traditional financial markets, including stocks, gold and oil prices and the technology proxied by hashrates and social sentiments gauged by Twitter data. The article fits the time series model using the Autoregressive Distributed Lag framework and selects the fitted model using penalized least-square estimates. The study also encompasses a set of control variables to improve the model’s accuracy. The findings confirm the existence of a dynamic relationship between the cryptocurrency market and the traditional financial markets. Our results imply that cryptocurrencies can be used as a tool for portfolio diversification. This study is a unique attempt at identifying the major drivers of the cryptocurrency market at large, by disentangling and modelling the dynamic relationships shared by the leading four cryptocurrencies with the relevant components of the financial markets. This article is a significant contribution to the emerging literature on cryptocurrencies and has implications for practitioners.
Rui Dias, Paulo Alexandre, Nuno Teixeira, Mariana Chambino
Green investors have expressed concerns about the environment and sustainability due to the high energy consumption involved in cryptocurrency mining and transactions. This article investigates the safe haven characteristics of clean energy stock indexes in relation to three cryptocurrencies, taking into account their respective levels of “dirty” energy consumption from 16 May 2018 to 15 May 2023. The purpose is to determine whether the eventual increase in correlation resulting from the events of 2020 and 2022 leads to volatility spillovers between clean energy indexes and cryptocurrencies categorized as “dirty” due to their energy-intensive mining and transaction procedures. The level of integration between clean energy stock indexes and cryptocurrencies will be inferred by using Gregory and Hansen’s methodology. Furthermore, to assess the presence of a volatility spillover effect between clean energy stock indexes and “dirty-classified” cryptocurrencies, the t-test of the heteroscedasticity of two samples from Forbes and Rigobon will be employed. The empirical findings show that clean energy stock indexes may offer a viable safe haven for dirty energy cryptocurrencies. However, the precise associations differ depending on the cryptocurrency under examination. The implications of this study’s results are significant for investment strategies, and this knowledge can inform decision-making procedures and facilitate the adoption of sustainable investment practices. Investors and policy makers can gain a deeper understanding of the interplay between investments in renewable energy and the cryptocurrency market.
Purpose This study aims to empirically investigate the linkages between digital trails of social signals (content and profile features of bitcoin-related tweets) and bitcoin price return using a VAR-BEKK-GARCH model. Design/methodology/approach Bitcoin-related tweets were collected every hour for six months from September 1, 2020, to February 29, 2021. The analysis involved two steps: first, examining tweet content, profiles, sentiment and emotions; and second, investigating the relationship between social signal volatility and hourly bitcoin price return. Findings Results indicate that bitcoin price changes can impact the sentiment expressed in tweets about bitcoin, and vice versa. While sadness exhibits a bidirectional volatility spillover with bitcoin, fear and anger display a one-period lag. Quartile analyses reveal that only fear in the second quartile shows a bidirectional spillover effect with bitcoin, while all other emotions except sadness demonstrate a unidirectional spillover effect in all remaining quartiles. Originality/value The study uses a novel two-step approach to analyze volatility spillovers between social signals and bitcoin price returns. Findings can guide investors and portfolio managers in making better allocation decisions and assist policymakers and regulators in reducing the adverse effects of bitcoin’s volatility on financial system stability.
We examine how the COVID-19 pandemic and Russia-Ukraine war affect volatility spillovers and extreme return movements in the stock, gold, and bitcoin markets. Our study uses the post-pandemic period of up to two and a half years in order to reflect the lingering effects of the pandemic as well as its initial impact. We find that volatility spillover has weakened in the post- versus pre-pandemic period. Additionally, our results suggest that the Russia-Ukraine war has had little impact on volatility spillovers. We subsequently test for extreme return movements separately and find substantial increases in the likelihood that two assets’ extreme returns move simultaneously post- versus pre-pandemic.
Despite being illegal in Morocco, bitcoin has gained great popularity in Morocco. However, in recent months, the Moroccan monetary authorities have set up two commissions to deal with crypto assets. The purpose of these commissions was to monitor international financial trends (in particular crypto assets). The current work will put forward a prospective study on the determinants that forms the price of bitcoin in Morocco, provided that the Moroccan monetary authorities would decide the legalization of the use of crypto assets. In an ARDL approach, an econometric model is applied to variables that reflect, not only all the factors related to the traditional currency, but also to variables that reflect specific factors to bitcoin over an eight-years period. This prospective study has highlighted that the frequency of bitcoin search on Google Trend, the number of bitcoins in circulation and the exchange rate between the Dollar and the Moroccan Dirham represent the main indicators that explain the formation of the price of bitcoin in the national territory.
Purpose This study reviews existing cryptocurrency research to provide answers to three puzzles in the literature. First, is cryptocurrency more like gold (i.e., a commodity) or should it be classified as a new financial asset? Second, can we apply our knowledge of the traditional capital market to the emerging cryptocurrency market? Third, what might be the future of cryptocurrency? Design/methodology/approach Bibliometric analysis is used to assess 2,098 finance-related cryptocurrency publications from the Web of Science (WoS) Core Collection database from January 2009 to April 2022. Three key research streams are identified, namely, (1) cryptocurrency features, (2) behaviour of the cryptocurrency market and (3) blockchain implications. Findings First, cryptocurrency should be viewed and regulated as a new asset class rather than a currency or a new commodity. While it can provide diversification benefits to the portfolio, cryptocurrency cannot work as a safe haven asset. Second, crypto markets are typically inefficient. Asset bubbles exist and are exacerbated by behavioural finance factors. Third, cryptocurrency demonstrates increasing potential as a medium of exchange and store of value. Originality/value Extant review papers primarily study one or two particular research topics, overlooking the interaction between topics. The few existing systematic literature reviews in this area typically have a narrow focus on trend identification. This study is the first study to provide a comprehensive review of all financial-related studies on cryptocurrency, synthesising the research findings from 2,098 publications to answer three cryptocurrency puzzles.
Farman Ullah Khan, Faridoon Khan, Parvez Ahmed Shaikh
Abstract The study aims at forecasting the return volatility of the cryptocurrencies using several machine learning algorithms, like neural network autoregressive (NNETAR), cubic smoothing spline (CSS), and group method of data handling neural network (GMDH-NN) algorithm. The data used in this study is spanning from April 14, 2017, to October 30, 2020, covering 1296 observations. We predict the volatility of four cryptocurrencies, namely Bitcoin, Ethereum, XRP, and Tether, and compare their predictive power in terms of forecasting accuracy. The predictive capabilities of CSS, NNETAR, and GMDH-NN are compared and evaluated by mean absolute error (MAE) and root-mean-square error (RMSE). Regarding the return volatility of Bitcoin and XRP markets, the forecasted results remarkably suggest that in contrast to rival approaches, the CSS can be an effective model to boost the predicting accuracy in the sense that it has the lowest forecast errors. Considering the Ethereum markets’ volatility, the MAE and RMSE associated with NNETAR are smaller than the MAE and RMSE of CSS and GMDH-NN algorithm, which ensures the effectiveness of NNETAR as compared to competing approaches. Similarly, in case of Tether markets’ volatility, the corresponding MAE and RMSE reveal that the GMDH-NN algorithm is an efficient technique to enhance the forecasting performance. We notice that no single tool performed uniformly for all cryptocurrency markets. The policymakers can adopt the model for forecasting cryptocurrency volatility accordingly.
Utilizing wavelet coherence analysis, we investigate the correlation of fluctuations and phase differences between Bitcoin and RMB to identify capital flows between the two currencies. The effects of Digital Currency Electronic Payment (DCEP) on their co-movement are further analyzed. Our findings reveal that the RMB exchange rate leads the price of Bitcoin in all significant co-movement areas. Furthermore, it appears that from February 2017 to September 2018, the Sino-US trade frictions and US dollar interest rate hikes may have resulted in a long-term negative co-movement, which seems to have been driven by RMB and possibly indicated capital flows from RMB to Bitcoin. The short-term positive co-movement between November 2019 and July 2020 could be attributed to the COVID-19 pandemic. Finally, we also demonstrate that the DCEP trial event has the potential to strengthen the positive co-movement between these currencies.
Kripto para piyasası ulaştığı işlem hacmiyle geleneksel para piyasasına rakip duruma gelmiştir. Kripto para piyasasında coinlere alternatif olarak altcoinler piyasaya sunulmuştur. Kripto para piyasasına binlerce coin ve altcoin sunulmasına karşın bitcoinin büyüklüğüne ulaşamamışlardır. Kripto piyasası tezgahüstü bir piyasadır. Bu piyasanın volatilitesi ve riski oldukça yüksektir. Bu piyasanın yüksek getiri imkanı vermesi nedeniyle yatırımcıların ilgi odağı olmaktadır. Çalışmanın amacı kripto para birimlerinin fiyat hareketliliği temel alınarak, bu kripto paralar arasındaki eş-bütünleşme ve nedensellik ilişkileri incelenmektedir. Çalışma kapsamındaki kripto paralar Johansen Eş-bütünleşme Analizi ve Granger Nedensellik Testi kullanılarak incelenmiştir. Johansen eş-bütünleşme test sonucunda iz istatistiği ve max öz değer istatistikleri %5 anlamlılık düzeyindeki kritik değerden yüksek olduğu, H0 hipotezinin reddedildiği ve kripto para serileri arasında eş-bütünleşme ilişkisinin bulunduğunu ortaya koymuştur. Granger Nedensellik Test sonuçları, ADA, BNB, DOGE ve ETH’nin BTC’nin ‘nedeni’ ve BTC’nin ADA, BNB, DOGE ve ETH’nin ‘nedeni’ olduğu ve aralarında çift taraflı bir ilişkisinin bulunduğu belirlenmiştir. ETH ve SOL’un BTC’nin ‘nedeni’ olduğu ve aralarında tek taraflı bir ilişkisinin olduğu görülmüştür. Anahtar Kelimeler: Cryptocurrency, Johansen Cointegration Analysis, Granger Causality Test
Due to recent developments, several countries have given the green light to cryptocurrencies, and big companies now accept them as a payment method. This article aimed to assess the random walk behaviour of the cryptocurrency market by analysing Bitcoin returns. The study observed daily Bitcoin closing prices from January 2016 to December 2023. We employed rigorous statistical tests, including the run test, generalised spectral test, automatic portmanteau test and wild bootstrap automatic variance ratio test. Furthermore, the rolling window technique was used to discern whether market efficiency was time-varying or static, involving dividing the data into four fixed rolling windows. Our overall empirical results revealed that Bitcoin price fluctuations were unpredictable, inferring market efficiency. The findings implied that Bitcoin prices adhered to a random walk pattern, making it challenging to identify abnormal trends in this emerging market.
Afees A. Salisu, Ahamuefula E. Ogbonna, Tirimisiyu F. Oloko
This study examines the effect of pandemic-induced uncertainty on cryptocoins (Bitcoin, Ethereum and Ripple). It employs the Westerlund and Narayan (2012, 2015) predictive model to examine the predictability of pandemic-induced uncertainty and our model's forecast performance. We examine the role of asymmetry in uncertainty and the sensitivity of our results to the recently-developed Salisu and Akanni (2020) Global Fear Index. Cryptocoins act as a hedge against uncertainty due to pandemics, albeit with reduced hedging effectiveness in the COVID-19 period. Accounting for asymmetry improves predictability and model forecast performance. Our results may be sensitive to the choice of measure of pandemic-induced uncertainty.