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.
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.
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.
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.
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.
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
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.
Cryptocurrencies have increasingly been traded against fiat currencies and as a result, governments globally have been trying to regulate these largely decentralized currencies. In this study, event study methodology has been used to evaluate the effect of regulatory announcements made by 25 countries. Based on cryptocurrency usage and returns of three major cryptocurrencies, namely Bitcoin, Ether, and XRP, this study finds that regulatory news results in significant abnormal returns for Bitcoin and Ether, but not for XRP. The authors find that irrespective of the type of news, abnormal returns are almost always negative. The countries have also been clustered based on their abnormal returns and it has been found that country characteristics such as income level, technological readiness and innovation potential affect the magnitude of abnormal returns. Thus, cryptocurrencies being global in essence, their regulatory oversight in countries do not exist in isolation, but they are also affected by the countries' development.
Abstract This study aims to evaluate forecasting properties of classic methodologies (ARCH and GARCH models) in comparison with deep learning methodologies (MLP, RNN, and LSTM architectures) for predicting Bitcoin's volatility. As a new asset class with unique characteristics, Bitcoin's high volatility and structural breaks make forecasting challenging. Based on 2753 observations from 08-09-2014 to 01-05-2022, this study focuses on Bitcoin logarithmic returns. Results show that deep learning methodologies have advantages in terms of forecast quality, although significant computational costs are required. Although both MLP and RNN models produce smoother forecasts with less fluctuation, they fail to capture large spikes. The LSTM architecture, on the other hand, reacts strongly to such movements and tries to adjust its forecast accordingly. To compare forecasting accuracy at different horizons MAPE, MAE metrics are used. Diebold–Mariano tests were conducted to compare the forecast, confirming the superiority of deep learning methodologies. Overall, this study suggests that deep learning methodologies could provide a promising tool for forecasting Bitcoin returns (and therefore volatility), especially for short-term horizons.
Abstract To measure the diversification capability of Bitcoin, this study employs wavelet analysis to investigate the coherence of Bitcoin price with the equity markets of both the emerging and developed economies, considering the COVID-19 pandemic and the recent Russia-Ukraine war. The results based on the data from January 9, 2014 to May 31, 2022 reveal that compared with gold, Bitcoin consistently provides diversification opportunities with all six representative market indices examined, specifically under the normal market condition. In particular, for short-term horizons, Bitcoin shows favorably low correlation with each index for all years, whereas exception is observed for gold. In addition, diversification between Bitcoin and gold is demonstrated as well, mainly for short-term investments. However, the diversification benefit is conditional for both Bitcoin and gold under the recent pandemic and war crises. The findings remind investors and portfolio managers planning to incorporate Bitcoin into their portfolios as a diversification tool to be aware of the global geopolitical conditions and other uncertainty in considering their investment tools and durations.
This study explores the dynamic co-movement of Islamic asset classes and cryptocurrencies for the period 01 March, 2017 to 15 June, 2022 by employing Wavelet methodology. The Islamic investment classes are represented by Islamic equities, Islamic Socially responsible investments, Real estate investment trusts and Sukuk. The results reveal that in normal times, there is negligible co-movement of both the asset classes. By contrast, both the investment classes exhibit significant spillover effect during the health crisis period. An important implication from these findings is that both the asset classes offer diversification opportunity during normal times but not during extreme times.
Cryptocurrencies are said to be very risky, and so are the currencies of emerging economies, including the South African rand. The steady rise in the movement of South Africans’ investments between the rand and BitCoin warrants an investigation as to which of the two currencies is riskier. In this paper, the Generalised Pareto Distribution (GPD) model is employed to estimate the Value at Risk (VaR) and the Expected Shortfall (ES) for the two exchange rates, BitCoin/US dollar (BitCoin) and the South African rand/US dollar (ZAR/USD). The estimated risk measures are used to compare the riskiness of the two exchange rates. The Maximum Likelihood Estimation (MLE) method is used to find the optimal parameters of the GPD model. The higher extreme value index estimate associated with the BTC/USD when compared with the ZAR/USD estimate, suggests that the BTC/USD is riskier than the ZAR/USD. The computed VaR estimates for losses of $0.07, $0.09, and $0.16 per dollar invested in the BTC/USD at 90%, 95%, and 99% compared to the ZAR/USD’s $0.02, $0.02, and $0.03 at the respective levels of significance, confirm that BitCoin is riskier than the rand. The ES (average losses) of $0.11, $0.13, and $0.21 per dollar invested in the BTC/USD at 90%, 95%, and 99% compared to the ZAR/USD’s $0.02, $0.02, and $0.03 at the respective levels of significance further confirm the higher risk associated with BitCoin. Model adequacy is confirmed using the Kupiec test procedure. These findings are helpful to risk managers when making adequate risk-based capital requirements more rational between the two currencies. The argument is for more capital requirements for BitCoin than for the South African rand.
This study investigates the hedge and safe-haven possibilities with bitcoin, gold and crude oil in different equity markets in the presence of time-varying market inefficiency. Our results indicate that periods of market inefficiency for the Bitcoin, gold and crude oil price positively influence their function as a hedge asset for the equity markets of Japan, China, the US, Europe and emerging countries. In addition to contributing to the discussion on the factors which affect the functioning of safe-haven assets, the empirical findings of this study further highlight the importance of market efficiency as a market microstructure feature. These results have important implications for investors seeking to manage risk through diversification across different asset classes.
Nader Naifar, Sohale Altamimi, Fatimah Alshahrani, Mohammed Alhashim
This paper aims to investigate the impact of global financial, economic, and gold price uncertainty indices (VIX, EPU, and GVZ) and investor sentiment based on media coverage news on the returns of Bitcoin and Ethereum during the COVID-19 pandemic. We adopt an asymmetric framework based on the Quantile-on-Quantile approach, which examines the quantiles of the cryptocurrency returns, investor sentiment, and the various uncertainties indicators. The empirical findings suggest that the COVID-19 pandemic has significantly impacted cryptocurrency returns. Specifically, (i) the results demonstrate the predictive power of Economic Policy Uncertainty (EPU) during this period, as evidenced by a strong negative association between EPU and cryptocurrency returns across all quantiles; ( ii ) the correlation between cryptocurrency returns and the VIX index was negative but weak, across various quantile combinations of Ethereum and Bitcoin returns; ( iii ) an increase in COVID-19 news negatively affected Bitcoin returns across all quantiles; ( iv ) Bitcoin and Ethereum cannot be relied upon as effective hedging tools against global financial and economic uncertainty during the COVID-19 pandemic. Studying the behavior of cryptocurrency during uncertainty like pandemics is extremely important because it provides investors with insights on diversifying their portfolios and hedging their risks.
This study extends the examination of the Efficient-Market Hypothesis in Bitcoin market during a five year fluctuation period, from September 1 2017 to September 1 2022, by analyzing 28,739,514 qualified tweets containing the targeted topic "Bitcoin". Unlike previous studies, we extracted fundamental keywords as an informative proxy for carrying out the study of the EMH in the Bitcoin market rather than focusing on sentiment analysis, information volume, or price data. We tested market efficiency in hourly, 4-hourly, and daily time periods to understand the speed and accuracy of market reactions towards the information within different thresholds. A sequence of machine learning methods and textual analyses were used, including measurements of distances of semantic vector spaces of information, keywords extraction and encoding model, and Light Gradient Boosting Machine (LGBM) classifiers. Our results suggest that 78.06% (83.08%), 84.63% (87.77%), and 94.03% (94.60%) of hourly, 4-hourly, and daily bullish (bearish) market movements can be attributed to public information within organic tweets.
Abstract The paper examines market co‐movement between pairs of financial assets in the time‐frequency domain. Recent finance literature confirms the integration of cryptocurrencies and financial assets, which may bring more investments with the possibility of surplus liquidity in the cryptocurrency segment, leading to financial instability. The novelty of this paper is examining the integration of cryptocurrencies and the indices of equity, sustainability, renewable energy, and crude oil for the daily observations from 2015 to 2021 by using the wavelet coherency method. The empirical results signify no integration in the short‐term scales and grow stronger in the medium‐term scales, especially during the COVID‐19 period, and further exhibit weaker heterogeneous associations in the long‐term scales. However, the sustainability, clean energy indices follow similar dynamics of the equity market and crypto pairs. In contrast, the global crude oil index showcases the minor integration with cryptocurrencies compared with other traditional asset classes. Hence, the cryptocurrency market fails to confirm the safe haven features, especially during the COVID‐19 periods (Medium‐term), which facilitate the domestic and international investors expecting to hedge their price risk in equity markets using cryptocurrencies may have to look for short‐term. The lead–lag heterogeneous effects of the asset‐pairs may pave arbitrage opportunities for investors.
Hashim Jusoh, Abdelkader O. El Alaoui, Amina Dchieche, Ahmad Faizol Ismail · 5 authors
We analyze the relationship between Bitcoin and major regional Islamic stock indices during two major events: COVID-19 and the Russia-Ukraine war. The multi-horizon analysis provide evidence of low correlation between Bitcoin’s inter-temporal returns and Islamic indices returns during periods before extreme events. However, there is limited potential for diversification in the long run as their correlations increase significantly. During shocks, Bitcoin cannot be a safe haven for Islamic markets.
This study aims to systematically analyze and synthesize the literature produced thus far on cryptocurrency investment. We use a systematic review process supported by VOSviewer bibliographic coupling to review 482 papers published in the ABS 2021 journal list, considering all different areas of knowledge. This paper contributes an in-depth systematic analysis on the unconsolidated topic of cryptocurrency investment through the use of a cluster-based approach grounded in a bibliographic coupling analysis, revealing complex network associations within each cluster. Four literature clusters emerge from the cryptocurrency investment literature, namely, investigating investor behavior, portfolio diversification, cryptocurrency market microstructure, and risk management in cryptocurrency investment. Additionally, the study delivers a qualitative analysis that reveals the main conclusions and future research venues by cluster. The findings provide researchers with cluster-based information and structured networking for research outlets and literature strands.
Surmounted environmental concerns and energy challenges have created an augmented awareness among the public and policymakers about alternate energy resources. Using a network approach, this paper aims to investigate the dependence between cryptocurrencies and the alternative energy market using data from January 1, 2018, to December 23, 2021. For this investigation, first, we build a static dependency network for a given set of variables using partial correlations. Then, we demonstrate within-system connections in a minimum spanning tree (MST) to assess the centrality of all variables. Finally, rolling-window estimations are made to exhibit time variations in both dependency and centrality networks. We find that clean alternative markets (SPGCE, ELEVHC & WILCE) and ETH are net risk transmitters to other markets and system-wide net contributors. We also demonstrate how SPGCE is essential for tying together the various parts of the networks and provide convincing evidence of time-varying within-system dependency. Our thorough examination of the dependency analysis offers significant insights to macroprudential regulators, policymakers, and portfolio managers, enabling them to safeguard the most vulnerable markets and choose the best legislative and policy measures to protect investors' interests in the face of unforeseen financial and economic conditions.
Dora Almeida, Andreia Dionísio, Paulo Ferreira, Isabel Vieira
Extraordinary events, regardless of their financial or non-financial nature, are a great challenge for financial stability. This study examines the impact of one such occurrence—the COVID-19 pandemic—on cryptocurrency markets. A detrended cross-correlation analysis was performed to evaluate how the links between 16 cryptocurrencies were changed by this event. Cross-correlation coefficients that were calculated before and after the onset of the pandemic were compared, and the statistical significance of their variation was assessed. The analysis results show that the markets of the assessed cryptocurrencies became more integrated. There is also evidence to suggest that the pandemic crisis promoted contagion, mainly across short timescales (with a few exceptions of non-contagion across long timescales). We conclude that, in spite of the distinct characteristics of cryptocurrencies, those in our sample offered no protection against the financial turbulence provoked by the COVID-19 pandemic, and thus, our study provided yet another example of ‘correlations breakdown’ in times of crisis.
This study aimed to uncover the impact of COVID-19 on the leading cryptocurrency (Bitcoin) and on sustainable finance with specific attention to their potential long memory properties. In this article, the application of the selected methodologies is based on a fractal and entropy analysis of the econometric model in the financial market. To detect the regularity/irregularity property of a time series, approximate entropy is introduced to measure deterministic chaos. Using daily data for Bitcoin and sustainable finance, namely DJSW, Green Bond, Carbon, and Clean Energy, we examine long memory behaviour by employing a rescaled range statistic (R/S) methodology. The results of the research present that the returns of Bitcoin, the Dow Jones Sustainability World Index (DJSW), Green Bond, Carbon, and Clean Energy have a significant long memory. Contrastingly, an interdisciplinary approach, namely wavelet analysis, is also used to obtain complementary results. Wavelet analysis can provide warning information about turmoil phenomena and offer insights into co-movements in the time–frequency space. Our findings reveal that approximate entropy shows crisis (turmoil) conditions in the Bitcoin market, despite the nature of the pandemic’s origin. Crucially, compared to Bitcoin assets, sustainable financial assets may play a better safe haven role during a pandemic turmoil period. The policy implications of this study could improve trading strategies for the sake of portfolio managers and investors during crisis and non-crisis periods.