This paper investigates the diversification, hedging, and safe-haven capabilities of Bitcoin and gold against blue economy and green finance assets using three different MGARCH models (DCC, ADCC, and GO-GARCH) during adverse events such as the COVID-19 health crisis and the 2022 Russia-Ukraine conflict. Blue economy assets, which refer to sectors that sustainably utilize ocean resources, are a key focus alongside green finance assets. The findings reveal that during crises, Bitcoin demonstrates robust safe-haven characteristics, particularly against blue economy assets like BJLE and OCEN. Conversely, gold exhibits pronounced safe-haven properties against specific blue economy and green finance assets such as BJLE and FAN. The GO-GARCH model highlights gold's strong diversification and safe-haven roles, especially against BJLE. Bitcoin, on the other hand, is more effective as a diversifier for PIO. Moreover, the GO-GARCH model consistently outperforms the DCC and ADCC models in terms of hedging effectiveness, showing that gold is the preferred hedging instrument for GNR and TAN, while Bitcoin is more effective for other blue and green assets. The results underscore the distinct roles of Bitcoin and gold in portfolio management strategies, offering insights for investors navigating market uncertainties in the context of sustainable investments.
In this article, we undertake a comprehensive empirical investigation to evaluate the predictive ability of 26 US macroeconomic indicators for the returns of six cryptocurrencies. The results of the in-sample test show that several macroeconomic variables, such as the three-month Treasury bill rate, default yield spread, term spread, and economic policy uncertainty index during the full sample period, as well as the term spread, VIX, three-month Treasury bill rate, dividend-price ratio, and economic policy uncertainty index during the monetary policy intervention period, show the ability to forecast returns for a minimum of two cryptocurrencies. We also find enhancements to these predictive capabilities during the monetary policy intervention period. The out-of-sample test validates the effectiveness of our in-sample findings and unveils additional macroeconomic variables that also have predictive ability. Our results show superior forecasting ability when utilizing these combination methods. Our findings also suggest that incorporating global factors, such as the MSCI All Country World Index, can improve forecasting ability. During the cryptocurrency bubble period, we find that the 10-year US government bond yield, dividend yield ratio, capacity utilization rate, and US economic policy uncertainty index can forecast at least two cryptocurrency returns. Furthermore, we observe that in addition to US macroeconomic variables, certain trade-related variables from other countries, such as the annual growth rates of export values for the United States, United Kingdom, and China, as well as the annual growth rates of import values for Japan and Italy, can forecast returns for at least two cryptocurrencies.
Hanan Haider Ali, Sumathi Kumaraswamy, Sara Al Balooshi, Yomna Abdulla
This study examines the news impact, persistence and asymmetric effects of stock, oil and cryptocurrency markets in Gulf Cooperation Council (GCC) countries. The diagonal BEKK method is applied to the daily trading prices of three major cryptocurrencies, crude oil and four stock market indices from January 2018 to February 2024. The empirical results indicate a strong, significant volatility spillover between cryptocurrencies, oil and stock prices, but no return spillover effect among these asset classes. A negative news shock in cryptocurrency markets generates more volatility in GCC stock prices than positive news. The study suggests that cryptocurrency price movements are independent of other asset classes, providing portfolio diversification opportunities for investors in GCC countries.
This research employs Multifractal Detrended Fluctuation Analysis (MFDFA) to investigate multifractal properties in financial variables, including Bitcoin prices and economic indicators. Spanning 2019–2022, the analysis reveals multifractal scaling not only in Bitcoin prices, but also in economic indicators such as inflation rates and energy commodity prices. The non-linear singularity spectra unveil the multifaceted nature of scaling properties. Temporal analysis exposes intriguing trends in multifractality with implications for market efficiency. Furthermore, correlation analysis unveils connections among multifractal properties. For instance, a positive correlation between oil prices and Bitcoin suggests similar market forces. The log-log plot of fluctuation function Fq versus lag size demonstrates a power-law relationship, characteristic of multifractal systems. The empirical data’s alignment in log-log space suggests self-similarity in the Bitcoin time series, supporting multifractality. The calculated Hurst exponents values suggest varying degrees of multifractality across the years, with 2021 exhibiting the highest degree and 2022 the lowest. Furthermore, an asymmetry index (0.5767) deviating from 0.5 indicates that the multifractal nature of the Bitcoin market is not symmetric. This research enhances risk assessment and portfolio optimization in finance. It challenges the Efficient Market Hypothesis (EMH), emphasizing the significance of MFDFA in comprehending financial market and economic factor’s relationships.
Purpose In recent years, traditional markets have been challenged by the emergence of cryptocurrencies, particularly when there are economic disruptions such as the COVID-19 pandemic. This paper examines the intricacies of financial dynamics during the COVID-19 crisis and explores their impact on traditional and cryptocurrency markets. In particular, we explore whether cryptocurrencies are a better safe haven of value than traditional financial markets during economic disruptions like that resulting from the COVID-19 pandemic. Design/methodology/approach This study examines the differential impacts of COVID-19 on cryptocurrency and traditional financial markets, utilising a robust difference-in-differences methodology to analyse market behaviours across two critical periods: before and after the pandemic was declared by the World Health Organization (WHO). This approach allows us to capture these markets’ distinct responses to a global economic shock, offering insights into their comparative resilience and volatility. The originality of our research lies in its comparative approach to an unprecedented global crisis, providing valuable insights into the dynamics of digital versus traditional assets under stress. Findings Our findings contribute to the academic discourse by revealing that, contrary to popular belief and compared to traditional assets, cryptocurrencies may not be a safe haven during crises and may begin to mimic traditional assets more closely during such periods. Originality/value This study enhances our understanding of asset behaviour during economic shocks and has significant implications for investors, policymakers and regulators as they navigate future financial crises.
Abstract This paper examines the dynamic interplay between the global geopolitical risk and eleven decentralized finance (DeFi) digital currencies during the inflationary burden caused by the Russia-Ukraine war episodes. Daily data spanning from 13 October 2021 to 29 October 2024 and the innovative Quantile-Vector Autoregressive (Q-VAR) methodology are employed for estimating the pairwise, joint and network linkages at the lower, middle and upper quantiles. High levels of geopolitical risk are more connected with bull markets of the DeFi assets and new war episodes strengthen this relation. Geopolitical tensions combined with high inflation lead to the GPR becoming major determinant of DeFi markets so contributing to the transition to the digital decentralized cashless financial system. Maker is the leading DeFi asset in this transition and constitutes a promising successor of fiat currencies that suffer from devaluation generated by conflicts.
2017'den sonra kripto para birimlerinde yaşanan fiyat dalgalanmaları, getiri fırsatları ve volatilite, yatırımcıların ilgisini çekerken; büyüyen işlem hacmi ve piyasa değeri, bu varlıkları geleneksel yatırımlara ek olarak yüksek kazanç ve portföy çeşitlendirme olanağı sunan bir seçenek haline getirmiştir. Buradan hareketle çalışmada piyasa değeri en yüksek üç kripto para biriminin (Bitcoin, Ethereum ve Tether USDt) 2017-2024 dönemi için volatilite düzeyleri asimetrik volatilite ölçüm modellerinden EGARCH modeli ile karşılıklı olarak incelenmektedir. EGARCH modellerine göre, Bitcoin ve Ethereum'da kötü haberler, getiri volatilitesini iyi haberlerden daha fazla etkilerken, kaldıraç etkisi gözlemlenmiştir. Buna karşılık, Tether USDt'de iyi haberlerin volatilite üzerindeki etkisi daha güçlü olup, anti-kaldıraç etkisi söz konusudur. Piyasadaki şokların, kripto paraların getiri volatilitesi üzerinde daha kalıcı bir etkiye sahip olduğu ve en çok Ethereum'un getiri oynaklığını etkilediği görülmektedir. Yarı ömür volatilite ölçüsü sonuçları, Bitcoin, Ethereum ve Tether USDt için sırasıyla 7 gün, 8 gün ve 74 gün olduğunu ortaya koymuştur. Bu durum, Bitcoin ve Ethereum’da yaşanan volatilitenin benzer sürede etkisinin kaybolduğunu, ama Tether USDt’de ise daha uzun sürdüğünü göstermektedir. Bunun sebebi Tether USdt kripto paranın stabil coin olmasıdır. Bu bağlamda, yatırımcılar ve portföy yöneticilerinin, kararlarını şekillendirirken kripto paraların asimetrik özellikleri ile oynaklık seviyelerini göz önünde bulundurmaları oldukça önemlidir.
The significant overflow of the cup, symbolizing the total capacity of the world’s real assets, with a superior flow of world financial assets leads to the ongoing spread of excess financial assets, inflating another financial bubble since 2008, in various directions. The ongoing unrestrained emission of money by the «golden antelope» represented by the Federal Reserve System leads to numerous market transformations that distort the picture of the equilibrium market, turning the world economy into a kingdom of crooked mirrors. Many countries could not decide for a long time on recognizing the legitimacy of cryptocurrency transactions at the state level. However, under the pressure of increasing volumes of financial flows generated at the instigation of the states themselves, more and more countries began to officially recognize cryptocurrency transactions. In 2024, Russia joined this list, which makes it relevant to analyze the likely impact of the global cryptocurrency market on the development of the national economy. The purpose of the presented studies is to analyze the expected impact of the development processes of global cryptocurrency markets on the domestic market. The scientific novelty of the obtained results lies in the analysis of the current state of affairs on cryptocurrency exchanges and their comparison with traditional exchanges, the speculative nature of crypto transactions, trading volumes that determine the size of cryptocurrency exchanges, indicators of manipulation in the cryptocurrency market, key results of trading on cryptocurrency exchanges, etc. The practical significance of the obtained results lies in the development of proposals to reduce the risks associated with cryptocurrency transactions that affect both the financial system of Russia and the economy of the country as a whole.
The aim of this research is to investigate the long-term relationships among the dollar exchange rate (TRY/USD), gold (GAU/USD), the Borsa Istanbul 100 Index (BIST 100) and the prices of Bitcoin (BTC/USD), Ethereum (ETH/USD), and Binance Coin (BNB/USD). Since the series contain structural breaks, Fourier unit root tests were used to model the structural breaks. As the method of this study, the relationships between variables in the long term were examined by using Fourier Shin (FSHIN) and Shin (1994) (SHIN) cointegration tests. The findings of this study showed that cryptocurrencies are cointegrated among themselves under structural breaks in the long term; investment instruments are cointegrated among themselves. In addition, as a result of this study, it was determined financial instruments and cryptocurrencies do not move in along over time under structural breaks.
Jahanzaib Alvi, Kehkashan Nizam, S. M. A. Jafri, Muhammad Rehan · 5 authors
This paper investigates the efficacy of deep learning models such as Long-Short Term Memory (LSTM), Convolutional Neural Networks (CNN), and Gated Recurrent Units (GRU) for cryptocurrency price prediction, examining their short-term and long-term forecasting accuracy for investor guidance and advancing AI in financial analysis. The study uses time series analysis with LSTM, CNN, and GRU models on daily cryptocurrency prices from Investing.com, preprocessing data before testing on Bitcoin, Ethereum Classic, Ethereum, Litecoin, Monero, and the other 37 cryptocurrencies. RMSE, MAE, and accuracy rates measure performance. Findings revealed that only six cryptocurrencies were selected for final analysis, including Bitcoin, Ethereum Classic, Ethereum, Litecoin, and Monero. Results indicate that the deep learning models, particularly the LSTM and GRU, can predict cryptocurrency prices with high accuracy, especially for short-term forecasts within a 7-day window. The CNN model demonstrates significant predictive power, suggesting its utility for immediate trading decisions. Across the models, short-term precision was remarkably high, while long-term predictions maintained a moderate level of accuracy. This study presents a comparative analysis of LSTM, GRU, and CNN models for forecasting cryptocurrency prices, emphasizing LSTM and GRU's ability to navigate price volatility and suggesting their use for real-time trading analysis. The study's historical data reliance curtails forecasting unforeseen market shifts. Future studies should include new variables like social sentiment and blockchain analytics and test real-time adaptive models to enhance predictive strength. Model validation in actual market conditions is recommended for practical application.
We develop a flexible supply and demand equilibrium framework that can be used to develop pricing models to forecast Bitcoin’s price trajectory based on its fixed, inelastic supply and evolving demand dynamics. This approach integrates Bitcoin’s unique monetary attributes with demand drivers such as institutional adoption and long-term holding patterns. Using the April 2024 halving as a baseline, we explore model scenarios with varying assumptions about growth in adoption and supply-side constraints, calibrated to real-world data. Our findings indicate that institutional and sovereign accumulation can significantly influence price trajectories, with increasing demand intensifying the impact of Bitcoin’s constrained liquidity. Forecasts suggest that modest withdrawals from liquid supply to strategic reserves could lead to substantial price appreciation over the medium term, while higher withdrawal levels may induce volatility due to supply scarcity. These results highlight Bitcoin’s potential as a long-term investment and underline the importance of integrating economic fundamentals into forward-looking portfolio strategies. Our framework provides flexibility for testing different market scenarios, demand curve functional forms, and parameterizations, offering a tool for investors and policymakers considering Bitcoin’s role as a strategic asset. By advancing a fundamentals-based approach, this study contributes to the broader understanding of how Bitcoin’s supply–demand dynamics influence market behavior.
Bu çalışma Bitcoin getirileri ile kripto para piyasalarındaki yatırımcı duyarlılığını temsil eden Kripto Korku ve Açgözlülük Endeksi arasındaki kısa ve uzun dönemli ilişkiyi ve bu ilişkinin yönünü ve şiddetini araştırmaktadır. Çalışmada 01.02.2018-07.09.2022 dönemine ait günlük veri setleri A-ARDL (Augmented Autoregressive Distributed Lag) yöntemi ile analize tabi tutulmuştur. Finansal stres ve VIX Korku endekslerinin de kontrol değişkenler olarak kullanıldığı çalışmada yatırımcı duyarlılığının Bitcoin getirilerini kısa ve uzun dönemde pozitif ve önemli seviyede etkilediği bulgusu elde edilmiştir. Buna göre açgözlülük (korku) duygusundaki artışın Bitcoin getirilerini pozitif (negatif) etkilediği belirlenmiştir. Elde edilen bu bulgunun davranışsal finans ve yatırımcı duyarlılığı teorileriyle uyumlu olduğu ifade edilebilmektedir.
Mohammad Abdullah, Mohammad Ashraful Ferdous Chowdhury, G. M. Wali Ullah
This study inspects the asymmetric tail risk dynamics, efficiency, and interconnectedness among FinTech stocks, cryptocurrencies, and traditional assets. Firstly, we employ the Multifractal-Asymmetric Detrended Cross-Correlation Analysis to examine the cross-correlation patterns and efficiency dynamics of the analyzed assets. The findings reveal asymmetries in cross-correlations and the presence of multifractality, highlighting the nonlinear relationships among these assets and find FinTech assets are the most efficient. Secondly, we utilize the time domain quantile connectedness method to investigate tail risk connectedness, offering insights into the network's shock transmission and spillover effects. Our analysis identifies the major risk transmitters (FinTech stocks) and receivers (bond), emphasizing the interconnectedness of the assets. Additionally, the study conducts bivariate portfolio analysis, considering short and long investment horizons, to guide asset allocation and hedging strategies. Our findings have significant implications for facilitating informed investment strategies and improving the stability and resilience of financial markets.
The study examines the relationship between volume and volatility in leading cryptocurrencies i.e. Bitcoin and Ethereum, within the framework of Mixture of Distribution Hypothesis (MDH). It accommodates structural shifts in the cryptocurrency prices and uses fat-tailed distributions. The results show that the MDH is rejected for both cryptocurrencies, and volume alone cannot explain the heteroskedasticity of returns; however, it acts as a significant predictor for volatility, especially when incorporating structural breaks in the model. Further, the forecasting performance improves when fat-tailed distributions, such as the skewed student’s t and Johnson’s Su distribution are used to model the innovations. Thus, volume holds important information in the crypto markets and can affect returns, thereby, raising concerns about market efficiency. Our results are robust across different periods, modelling approaches and forecasting horizons, and hold substantial implications for traders, market participants, regulators, and governments in designing effective policies.
Saralees Nadarajah, Jules Clément, Patrick Rakotomarolahy, Henri T. J. E. Ratolojanahary
The purpose of this study is to conduct an empirical comparative study of volatility models for three of the most popular cryptocurrencies. We study the volatility of the following cryptocurrencies: Bitcoin, Ethereum, and Litecoin. We consider the GARCH-type, boosting-family-tree-based ensemble learning, and ANFIS volatility models for these financial crypto-assets, which some have claimed capture stylized facts about cryptocurrency volatility well. We conduct comparative studies on in-sample and out-of-sample empirical analyses. The results show that tree-based ensemble learning delivers better forecast accuracy. Nevertheless, the performance of some GARCH-type volatility models is relatively close to that of the best model on both training and evaluation samples.
Purpose The study aimed to determine the static return connectedness between Brazil, Russia, India, China and South Africa (BRICS) equity markets and crypto assets. Design/methodology/approach The study employs the time-varying parameter vector autoregression (TVP-VAR) method to examine the static and dynamic connectedness between crypto assets and the BRICS stock market. The study sample size was segmented into full sample, pre-COVID-19 and post-COVID-19 for in-depth analysis. Findings Empirical findings pointed out the significant rise in the total connectedness between both markets in the pre-COVID-19 period. Our result also exhibits a lower level of connectedness during the post-COVID-19 period. During the full sample period, it was found that cryptocurrencies and Indian, Chinese and South African stock markets remained key return transmitters, while Russian and Brazilian stock markets were seen as recipients. Moreover, during the pre-COVID period, cryptocurrencies played the role of return transmitter while the stock markets in BRICS remained recipients of return spillover. Practical implications This study contains practical insights for investors and portfolio managers in diversifying their portfolios considering the aforementioned connectivity of both markets, especially during periods of instability. Originality/value The study highlighted the importance of the TVP-VAR method in analyzing the static and dynamic connectedness of returns between cryptocurrencies and BRICS stock markets in different periods, including pre- and post-COVID-19. It further pragmatized the dynamic roles of cryptocurrencies as transmitters of returns and the BRICS stock markets as receivers where investors and policymakers can navigate market uncertainties.
Abstract The study attempts to add to the existing literature on the relationship between uncertainties and Bitcoin by determining the direction of the relationship between Economic Policy Uncertainty (EPU), Geopolitical Risk (GPR), Political Risk (PR) and Bitcoin returns. This is to ascertain if Bitcoin hedges and is a safe haven asset against uncertainties. We employed the use of Ordinary Least Square (OLS), Autoregressive Distributive Lag (ARDL) and Quantile Regression (QR) to achieve the research objective. Having discovered the existence of structural breaks after conducting the Zivot-Andrews unit root for structural breaks, the analysis was divided into full sample period, first sub-period and the second sub-period. Findings show that EPU, GPR and PR hedge and play the role of safe haven against uncertainties in the United States (US). We found that EPU exerts positive influence against Bitcoin returns while GPR and PR negatively influence Bitcoin returns. The result further shows that Bitcoin returns hedges against EPU in the lower and middle quantiles while Bitcoin returns hedges against PR only in the lower quantile. The study therefore concludes that uncertainty and risk in the US influence bitcoin returns. It supports the hedging ability and safe haven properties of bitcoin, emphasising that bitcoin returns react more to EPU US than GPR US and PR US, therefore recommending investment experts and financial analysts focus more on EPU US than GPR US and PR US.
This study is the first to scientifically investigate stock indexes and currency exchanges that affect crypto prices. The purpose is to distinguish between the USA-Japan stock markets and the currency market's short- and long-term effects on bitcoin and ethereum. Auto Regressive Distributed Lag (ARDL) is used to analyze weekly series from 1-1-2016 to 20-10-2024. An asymmetric error-checking framework employing non-linear ARDL statistical approach to study variables affecting bitcoin and ethereum prices. Bitcoin appear to have short- and long-term linear effects on the US-Japan stock markets. Euro, GBP, and USA-Japan stock markets exhibit short-term linear effects with ethereum. Ethereum linearly affects GBP. This research helps exchange brokers and crypto traders diversify their holdings, reduce stock index and currency exchange risk, and accurately predict bitcoin and ethereum price variations.
We explore volatility spillover effects between mainstream cryptocurrencies and energy token markets in the 120 days following three notable Blockchain bridge heists in 2022. Using the DCC-GARCH model, we find significant spillover effects between Bitcoin, Ethereum, and energy tokens like Power Ledger Token and Energy Web Token post-heists. This indicates heightened investor concern and panic trading impacting cryptocurrencies and energy token markets. Our analysis also reveals a herding behaviour in energy tokens under market stress and increased liquidity issues, leading to broader market quality deterioration. Based on these findings, we propose regulatory enhancements and the ‘Energy Future Fund’ to support the stability and growth of energy token markets.