ABSTRACT This paper examines the shifts in investor sentiment during the Russia–Ukraine war and its consequent impact on market volatility. By employing a comprehensive dataset that includes the S&P 500 index, historical Bitcoin prices, the Investor Sentiment Index, the Industrial Production Index, and the US Consumer Price Index, this study applies several econometric models such as generalized autoregressive conditional heteroskedasticity (GARCH) models, regression analyses, vector error correction models (VECM), and the Granger causality model. The analysis spans from January 2021 to March 2023. The findings indicate that investor sentiment significantly influences returns in both stock and cryptocurrency markets, having a positive effect. These results underscore the importance for investors and policymakers to monitor investor sentiment during periods of conflict to understand its potential impact on financial markets. This research offers valuable insights that can guide investment decisions and inform policy interventions.
David Iheke Okorie, Joel Miworse Gnatchiglo, Presley K. Wesseh
Active cryptocurrency mining and trading comes with heavy electricity demand and increased emissions. Thus, cryptocurrency mining is prohibited in most economies. Consequently, miners relocate to regions or economies without these prohibitions and/or with relatively lower electricity rates. As such, presenting a nexus between the cryptocurrency and electricity markets, even at the global level. This article investigates the different forms of relationships existing between these markets. The conditional asymmetric volatility model with the Wald, nonparametric and parametric Granger causality tests are employed. The results confirm the existence of both unidirectional and bidirectional lead-lag return relationships between the cryptocurrency and electricity markets. Cryptocurrency returns drive electricity demand. This finding is homogeneous both on a global and strata (homogeneous groupings) basis. Also, the electricity market spills over significant volatilities to the cryptocurrency markets without feedback, nonetheless. Result-based policies are recommended towards green finance, decarbonization, and emission mitigations through the demand for electricity by the cryptocurrency markets. They include the use of clean and renewable electricity sources and technologies for cryptocurrency market activities.
Decentralized finance (DeFi) is a brand-new disruptive procedure that encourages the use of blockchain technology for developing and distributing a variety of financial goods and services. This study investigates the time-varying and asymmetric interplay between DeFi and CEE stock returns, concentrated around the COVID-19 outbreak and the Russo-Ukrainian conflict. While the associations between other cryptocurrencies and conventional assets have been studied, DeFi assets have not. For this purpose, we employ the multivariate DECO-GARCH model and cross-quantilogram framework. The results reveal a positive equicorrelation between DeFi and CEE stock market returns. Notably, the influence of DeFi on CEE stock markets is greater during the COVID-19 outbreak and the Russo-Ukrainian conflict than in the other periods. Furthermore, the cross-quantilogram estimations uncover that CEE stock markets depend less on the DeFi market at longer lag lengths. This means that the diversification benefits of DeFi against CEE stock market returns are more important for long-run investment horizons. In general, our research offers a new understanding of dependence structures, which might help investors make better investment decisions and direct their trading strategies.
R. C. Bose, Jeevan Nagarkar, Sushant Malik, Nisha Bharti
Predictability of the various financial instruments can lead to more trust and investment. The study examines the long-term and causal relationship between various Nifty indices and the Ethereum cryptocurrency. This study considers the data from April 2015 to December 2022 in two phases, pre-covid and post-covid. Johansen’s cointegration test was used to determine if the vectors in the data set are cointegrated, using the Max-Eigen and Trace tests for evaluation. The Granger causality test was also used to explore the short-term causal relationship between Ethereum and the five Nifty indices. The study found that post-pandemic daily returns of stock market indices have developed a significant cointegration with the cryptocurrency over time. The Granger causality test results showed bi-directional relationships of Nifty 50, Nifty 200 and Nifty Next 50 with Ethereum and a unidirectional relationship between Nifty Auto and Ethereum. The non-linear results reveal a one-way relationship pre-covid and a bi-directional relationship post-covid except for Nifty Banks. Johansen’s cointegration test, both in the pre-and post-covid-era, indicated that these indices had a substantial long-term cointegration with cryptocurrencies. This study also offers guidance to investors in making long-term investment decisions and to regulatory authorities. This implied that the investing decisions resulted in developing a causal relationship between the equity market and cryptocurrencies, which seemed very unlikely before 2020. This indicates that a new and young investor also considered cryptocurrencies a viable alternate investment option compared to traditional options such as fixed deposits, gold, and other fixed-income instruments.
This research analyzes the dynamic relationships between the economic and political uncertainty index and the fear index in global markets and cryptocurrencies using the wavelet-based DCC-GARCH method, considering different time scales. Monthly data sets for the periods 2012–April 2024 for GEPU,VIX, and Bitcoin and April 2016–April 2024 for Ethereum are used in the study. Findings are obtained in terms of the volatility interaction between cryptocurrencies (Bitcoin and Ethereum) and GEPU and VIX, as well as four different time scales representing the short, medium, and long term. As a result of the analysis based on raw data, it was found that there is no volatility interaction between cryptocurrencies and GEPU and VIX returns. However, there is a volatility interaction between past volatility shocks and current period volatility shocks in the 4-8 and 16-32 month investment cycle periods of VIX, Bitcoin, GEPU, and Ethereum and time scales. These results, which show that volatility shocks persist in both 4-month and 16-month investment cycles, have significant implications for investors and policymakers. They highlight the need for comprehensive information about changes in the global economy and politics, and they are expected to provide insights for both investors and policymakers.
Majid Mirzaee Ghazani, Ali Akbar Momeni Malekshah, Reza Khosravi
Abstract We used daily return series for three pairs of datasets from the crude oil markets (WTI and Brent), stock indices (the Dow Jones Industrial Average and S&P 500), and benchmark cryptocurrencies (Bitcoin and Ethereum) to examine the connections between various data during the COVID-19 pandemic. We consider two characteristics: time and frequency. Based on Diebold and Yilmaz’s (Int J Forecast 28:57–66, 2012) technique, our findings indicate that comparable data have a substantially stronger correlation (regarding return) than volatility. Per Baruník and Křehlík’ (J Financ Econ 16:271–296, 2018) approach, interconnectedness among returns (volatilities) reduces (increases) as one moves from the short to the long term. A moving window analysis reveals a sudden increase in correlation, both in volatility and return, during the COVID-19 pandemic. In the context of wavelet coherence analysis, we observe a strong interconnection between data corresponding to the COVID-19 outbreak. The only exceptions are the behavior of Bitcoin and Ethereum. Specifically, Bitcoin combinations with other data exhibit a distinct behavior. The period precisely coincides with the COVID-19 pandemic. Evidently, volatility spillover has a long-lasting impact; policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks (e.g., the COVID-19 pandemic) and simultaneously reduce its side effects.
This paper examines the correlation between three prospective investing options: the Bitcoin cryptocurrency price, gold, and the Dow Jones stock index. The main research question is whether there is a causal effect of gold and the DWJ on Bitcoin and how this effect varies on time. The study begins with a background analysis that explains the definitions and operation of cryptocurrencies, followed by a brief overview of gold and its derivatives. In addition, a historical review of stock markets is provided, with a focus on the Dow Jones index. Then, a literature review follows. Daily data from three separate periods are used, each spanning four years. The first period, running from October 2014 to September 2018, provides an overview of the introduction of official cryptocurrency price data. The second period, running from Oct 2018 to Sept 2022, captures more recent trends preceding COVID-19. The third period, from January 2020 to December 2023, is the whole COVID-19 period with the initiation, embedded, and terminal phases. Classical inductive statistical methods (descriptive, correlations, multiple linear regression) as well as time series analysis methods (autocorrelation, cross-correlation, Granger causality tests, and ARIMA modeling) are used to analyze the data. Rigorous testing for autocorrelation, multicollinearity, and homoskedasticity is performed on the estimated models. The results show a correlation of Bitcoin with gold and the DWJ. This correlation varies over time, as in the first period the correlation mainly concerns the DWJ and in the second it mainly concerns gold. By using ARIMA models, it was possible to make a forecast in a time horizon of a few days. In addition, the structure of the forecasting mechanism of gold and DWJ on Bitcoin seems to have changed during the COVID-19 crisis. The findings suggest that future research should encompass a broader dataset, facilitating comprehensive comparisons and enhancing the reliability of the conclusions drawn.
This study aims to investigate the information spillover among four traditional financial assets (i.e., crude oil, gold, stock, and U.S. dollar) and nine main cryptocurrencies (i.e., Bitcoin, Cardano, Dai, Ripple, Dogecoin, Ethereum, Ethereum Classic, Monero, and Tether), by constructing entropy-based information spillover network and information integration network from both static and dynamic perspectives. The empirical results show that the information spillover among these assets is time-varying, experiencing an obvious increase trend after the COVID-19. As a whole, traditional financial assets mainly play the role of net information transmitter while cryptocurrencies mainly play the role of net information recipient. Tether and Dai are the two main visual coins that can transmit net information flow to traditional assets, while gold and stock are the two main traditional assets that transmit net information flow to cryptocurrencies. Tether and U.S. dollar are the central nodes that link traditional financial assets and cryptocurrencies together.
Objective: This study investigates how Environmental, Social, and Governance (ESG) criteria can be integrated into cryptocurrency portfolio strategies, evaluating their performance across different market conditions and time periods. Theoretical Framework: This research is based on Modern Portfolio Theory (MPT) and principles of ESG investing. The study uses Markowitz's mean-variance optimization and the triple bottom line approach to understand the benefits of ESG integration in investment strategies. Method: The research involves a comparative analysis of various cryptocurrency portfolio strategies, including Buy-and-Hold, Simple Moving Average (SMA), MinVar, and MaxSharpe. Data was collected daily from October 1, 2016, to September 31, 2021. The study uses mean-variance analysis to assess risk-return profiles, incorporating ESG factors into the evaluation framework. Results and Discussion: The results show that the Buy-and-Hold strategy consistently yielded the highest returns across most portfolios. However, during volatile periods, strategies like MinVar and MaxSharpe provided better risk-adjusted returns. The discussion contextualizes these results within the theoretical framework, highlighting how ESG integration enhances risk management and aligns investments with sustainable development goals (SDGs). Research Implications: This research suggests that integrating ESG criteria into cryptocurrency portfolios can improve risk management and align investments with sustainability goals. These findings have practical implications for investment strategy development and sustainable finance practices. Originality/Value: This study offers a unique analysis of cryptocurrency portfolio strategies that incorporate ESG criteria. Its findings are relevant for influencing sustainable investment practices and optimizing cryptocurrency portfolios in line with ESG principles.
The emergence of Bitcoin Exchange-Traded Funds (ETFs) marks a significant milestone in the evolution of cryptocurrency investment. This research paper investigates the potential impact of Bitcoin ETFs on existing cryptocurrency exchanges, focusing on liquidity dynamics and institutional investment trends. By examining the mechanisms through which ETFs may draw liquidity away from direct Bitcoin exchanges, this study aims to shed light on the evolving landscape of cryptocurrency trading. Additionally, the paper explores the potential for Bitcoin ETFs to attract increased institutional investment in the cryptocurrency market, analyzing the factors that may contribute to this trend. Through a comprehensive analysis of market data and expert insights, this research provides valuable insights into the evolving relationship between Bitcoin ETFs and traditional cryptocurrency exchanges.
This paper uses a state-dependent local projection model to empirically test the dynamic risk performance of cryptocurrency assets under geopolitical risk events, and to examine whether they have safe-haven properties in the face of major global external shocks. We demonstrate that the volatility of the cryptocurrency market exhibits a non-linear relationship with geopolitical risk. They are uncorrelated in normal times, but the risk of cryptocurrency market rises significantly under extreme geopolitical risk events. The cumulative impulse response pattern of volatility in the cryptocurrency market is similar to that of volatility in speculative assets such as stocks and bonds, but negatively correlated with that of volatility in safe-haven assets such as gold and the U.S. dollar. Our findings suggest that the volatility of cryptocurrencies should not be underestimated when investors consider hedging strategies under external shocks.
Digital assets and the traditional financial sectors are increasingly interacting. In the field of cryptocurrency, the combination with the sports industry has received attention from fans and clubs, media, and academic circles. Based on the LASSO-VAR model which is suitable for large samples, this article investigates the connectivity between sports cryptocurrency Chiliz (CHZ) and listed European football club stocks. The results show that there is a certain interrelatedness among the selected assets, and it becomes closer with the outbreak of major emergencies. In addition, CHZ is a net recipient of spillovers, while club stocks show diversification. Our results are helpful for club fans, individual and institutional investors, market regulators, and policymakers to make correct decisions.
Cryptoassets are extremely volatile with possible volatility jumps and infrastructure noise, making the estimation of true volatility process challenging. When the high-frequency data are not available, the true volatility needs to be estimated to be further studied or forecasted. The GARCH-family models have become a norm in the field. Here, we examine the performance of 6 GARCH-type specifications with 4 distributional assumptions and compare them with 4 non-parametric range-based models built on the daily ‘candles’. Our study focuses on five popular cryptocurrencies (Bitcoin, Ethereum, BNB, XRP, and Dogecoin) between 1 July 2019 and 30 September 2022, utilizing Binance 5-minute data for realized measures as the high-frequency estimators of the true volatility process. The results reveal that the Garman-Klass estimator clearly outperforms the GARCH-family models in all studied settings, and the other range-based estimators remain competitive with the GARCH-family models. These results are crucial for studies on volatility in cryptoassets where using the GARCH-type models is a standard. When the high-frequency data are not available, the range-based estimators, and the Garman-Klass estimator in particular, should be preferred as proxies for the true volatility process over the GARCH-type models, be it in the in-sample, more qualitative studies, or the forecasting, out-of-sample exercises.
Purpose The purpose of this study is to examine Bitcoin's price behavior across market conditions, focusing on the influence of Bitcoin's historical prices, news sentiment and market indicators like oil prices, gold and the S&P index. The authors also assess the stability of Bitcoin-inclusive hedging portfolios under different market conditions, for example, bearish, bullish and moderate market states. Design/methodology/approach This study uses the Quantile Autoregressive Distributed Lag model to explore the effects of different factors on Bitcoin's prices across various market situations. This method allows for a detailed analysis of historical trends, investor expectations and external market influences on Bitcoin's price movements and systematic stability. Findings Key findings reveal historical prices and investor expectations significantly influence Bitcoin in all market scenarios, with news sentiment exhibiting substantial volatility. This study indicates that oil prices have minimal impacts on Bitcoin, whereas gold is a stabilizing asset in bear markets, with the S&P index influencing short-term fluctuations. At the same time, Bitcoin's volatility varies with market conditions, proving more efficient as a hedging tool in bear and stable markets than in bull ones. Originality/value This study highlights the intrinsic correlation between Bitcoin's prices, news sentiment and financial market indicators, enhancing understanding of Bitcoin's market dynamics. The authors demonstrate Bitcoin's weak direct correlation with commodities like oil, the stabilizing role of gold in crypto portfolios and the stock market's indirect effect on Bitcoin prices. By examining these factors' impacts across various market conditions, the findings offer strategies for investors to improve hedging and portfolio management in cryptocurrency markets.
This study investigates the impact of market liquidity on Bitcoin’s diversification performance against global equity risk, using gold as a comparative benchmark. By using hourly data to construct the liquidity measures, our findings suggest that an increase in Bitcoin’s market liquidity significantly enhances Bitcoin’s effectiveness as a diversifier relative to gold.
We examine the linkages between Bitcoin, Ethereum, Tether and USD Coin returns—in the context of the post-epidemic landscape. Our paper utilizes the underpinning concept of the fractal market hypothesis. By employing our theoretical nexus alongside the continuous wavelet transform, we elucidate the changes in investor demand during notable events. Our results suggest that all four cryptocurrencies exhibited significant volatility in response to the March 2020 pandemic and the Delta variant announcements. Moreover, the fall of Three Arrows Capital and FTX affected all cryptocurrencies, while Silicon Valley Bank’s liquidation only impacted the stablecoins. Pairwise, investors heavily demand stablecoins during financial turmoil, which causes unconditional synchronization. The two cryptocurrency categories are anti-phasing in a market regime where traders are widely unanimous, but in-phasing when the sentiment consists of divergent perceptions. These cross-cryptocurrency phases of demand persist in the long run for USD Coin but are only medium-term for Tether.
The linkage between the metal market and crypto assets is a topic of concern. Utilizing a novel TVP-VAR framework, this study examines the volatility transmission between NFTs and precious/industrial metals. The results show strong connectivity, with most NFTs being net transmitters, and industrial metals being mostly net spillover recipients. Moreover, the connectivity was affected by the COVID-19 epidemic and the Russia-Ukraine War. Considering the potential reference value that this empirical fact may bring to market participants, this paper divides the time samples and uses the DCC-GARCH t-copula method to analyse the portfolio construction of every sub-sample. The research investigates and compares the hedge ratio, optimal weights, and hedging effectiveness of NFT-industrial metals and NFT-precious metals. These findings can bring potential inspiration to investors, market regulators, and policymakers.
Purpose Sustainable development hinges on a crucial shift to renewable energy, which is essential in the fight against global warming and climate change. This study explores the relationships between artificial intelligence (AI), fuel, green stocks, geopolitical risk, and Ethereum energy consumption (ETH) in an era of rapid technological advancement and growing environmental concerns. Design/methodology/approach This research stands at the forefront of interdisciplinary research and forges a path toward a comprehensive understanding of the intricate dynamics governing green sustainability investments. These objectives have been fulfilled by implementing the innovative quantile time-frequency connectedness approach in conjunction with geopolitical and climate considerations. Findings Our findings highlight coal market dominance and Ethereum energy consumption as critical short- and long-term market volatility sources. Additionally, geopolitical risks and Ethereum energy consumption significantly contribute to volatility. Long-term factors are the primary drivers of directional volatility spillover, impacting green stocks and energy assets over extended periods. Additionally, SHapley Additive exPlanations (SHAP) findings corroborate the quantile time-frequency connectedness outcomes. Research limitations/implications This study highlights the critical importance of transitioning to sustainable energy sources and embracing digital finance in fostering green sustainability investments, illuminating their roles in shaping market dynamics, influencing geopolitics and ensuring the long-term sustainability required to combat climate change effectively. Practical implications The study offers practical sustainability implications by informing green investment choices, strengthening risk management strategies, encouraging interdisciplinary cooperation and fostering digital finance innovations to promote sustainable practices. Originality/value The implementation of the quantile time-frequency connectedness approach, in line with considering geopolitical and climate factors, marks the originality of this paper. This approach allows for a dynamic analysis of connectedness across different distribution quantiles, providing a deeper understanding of variable interactions under varying market conditions.
Bitcoin price volatility fascinates both researchers and investors, studying features that influence its movement. This paper expends on previous research and examines time series data of various exogenous and endogenous factors: Bitcoin, Ethereum, S&P 500, and VIX closing prices; exchange rates of the Euro and GPB to USD; and the number of Bitcoin-related tweets per day. A period of three years (from September 2019 to September 2022) is covered by the research dataset. A two-layer framework is introduced tasked with accurately forecasting Bitcoin price. In the first layer, to account for complexities in the analyzed data, variational mode decomposition (VMD) extracts trends from the time series. In the second layer, Long short-term memory and hybrid Bidirectional long short-term memory networks were used to forecast prices several steps ahead. This work also introduced an enhanced variant of the sine cosine algorithm to tune the control parameters of VMD and both neural networks for attaining the best possible performance. The main focus is on combining VMD with modified metaheuristics to improve cryptocurrency closing value forecast. Two sets of experiments were conducted, with and without VMD. The results have been contrasted with models tuned by seven other cutting-edge optimizers. Extensive experimental outcomes indicate that Bitcoin price can be forecasted with great accuracy using selected features and time series decomposition. Additionally, the best model was analyzed, and Shapley values indicated that features such as EUR/USD exchange rates, Ethereum closing prices, and GBP/USD exchange rates, have a significant impact on forecasts.