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Purpose: This paper aims to analyze the connectedness between Gulf Cooperation Council (GCC) stock market index and cryptocurrencies. It investigates the relevant impact of RavenPack COVID sentiment on the dynamic of stock market indices and conventional cryptocurrencies as well as their Islamic counterparts during the onset of the COVID-19 crisis. Design/methodology/approach: The authors rely on the methodology of Diebold and Yilmaz (2012, 2014) to construct network-associated measures. Then, the wavelet coherence model was applied to explore co-movements between GCC stock markets, cryptocurrencies and RavenPack COVID sentiment. As a robustness check, the authors used the time-frequency connectedness developed by Barunik and Krehlik (2018) to verify the direction and scale connectedness among these markets. Findings: The results illustrate the effect of COVID-19 on all cryptocurrency markets. The time variations of stock returns display stylized fact tails and volatility clustering for all return series. This stressful period increased investor pessimism and fears and generated negative emotions. The findings also highlight a high spillover of shocks between RavenPack COVID sentiment, Islamic and conventional stock return indices and cryptocurrencies. In addition, we find that RavenPack COVID sentiment is the main net transmitter of shocks for all conventional market indices and that most Islamic indices and cryptocurrencies are net receivers. Practical implications: This study provides two main types of implications: On the one hand, it helps fund managers adjust the risk exposure of their portfolio by including stocks that significantly respond to COVID-19 sentiment and those that do not. On the other hand, the volatility mechanism and investor sentiment can be interesting for investors as it allows them to consider the dynamics of each market and thus optimize the asset portfolio allocation. Originality/value: This finding suggests that the RavenPack COVID sentiment is a net transmitter of shocks. It is considered a prominent channel of shock spillovers during the health crisis, which confirms the behavioral contagion. This study also identifies the contribution of particular interest to fund managers and investors. In fact, it helps them design their portfolio strategy accordingly. © 2023, Hayet Soltani, Jamila Taleb and Mouna Boujelbène Abbes.
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The purpose of this study was to identify and measure the impact of the different effects of entropy states over the high-frequency trade of the cryptocurrency market, especially in Bitcoin, using and selecting optimal parameters of the Bayesian approach, specifically through approximate Bayesian computation (ABC). ABC corresponds to a class of computational methods rooted in Bayesian statistics that could be used to estimate the posterior distributions of model parameters. For this research, ABC was applied to estimate the daily prices of the Bitcoin cryptocurrency from May 2013 to December 2021. The findings suggest that the behaviour of the parameters for our tested trading algorithms, in which sudden jumps are observed, can be interpreted as changes in states of the generated time series. Additionally, it is possible to identify and model the effects of the COVID-19 pandemic on the series analysed in the research. Finally, the main contribution of this research is that we have characterised the relationship between entropy and the evolution of parameters defining the optimal selection of trading algorithms in the financial industry. [ FROM AUTHOR] Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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This research investigates the effects of several measures of Twitter-based sentiment on cryptocurrencies during the COVID-19 pandemic. Innovative economic, as well as market uncertainty measures based on Tweets, along the lines of Baker et al. (2021), are employed in an attempt to measure how investor sentiment influences the returns and volatility of major cryptocurrencies, developing on non-linear Granger causality tests. Evidence suggests that Twitter-derived sentiment mainly influences Litecoin, Ethereum, Cardano and Ethereum Classic when considering mean estimates. Moreover, uncertainty measures non-linearly influence each cryptocurrency examined, at all quantiles except for Cardano at lower quantiles, and both Ripple and Stellar at both lower and higher quantiles. Cryptocurrencies with lower values are found to be unaffected by investor sentiment at extreme values, however, prove to be profitable due to more aligned investor behaviour.
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Everybody is talking about cryptocurrencies. These digital tokens, which started in a one-asset market, have swiftly ballooned into a massive and diverse "cryptomarket. " The cryptomarket is still mostly unregulated, but this is about to change. With President Biden's adoption of the Executive Order on Ensuring Responsible Development of Digital Assets, regulatory initiatives are being adopted abroad, and global regulation looms ahead. In light of the expected regulatory changes, two important questions emerge: is there a clear rationale for legal intervention in the cryptomarket? And if so, what type of regulation is optimal?This Article is the first to consider how to regulate the cryptomarket through an empirical analysis of how the COVID-19 crisis affected the cryptomarket. We take a two-step approach to answer these pivotal questions. First, we analyze empirical evidence from the early days of the COVID-19 pandemic to better understand the risks posed by the cryptomarket when a crisis emerges. Second, we apply a law-and-economics approach to identify which market failures are consistent with the data and derive novel regulatory lessons. Our empirical analysis reveals an interesting pattern: investors initially shifted funds to the cryptomarket when the pandemic erupted, but then made a U-turn and diverted funds out of cryptocurrencies, leading to a plunge in the market. We maintain that such investor behavior can have both rational and behavioral explanations, which in turn affects the optimal choice of regulation.Accordingly, we map each rational and behavioral explanation onto potential market failures by surveying different possible interpretations of our findings, such as substitution effects between traditional markets and the cryptomarket, exploitation of investors in the form of pump-and-dump schemes, and other criminal activities. We then discuss how each type of failure can serve as justification for regulation and derive regulatory lessons on how to best intervene in the cryptomarket depending on the source of the market failure.
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Using daily data from the beginning of 2018 to the end of 2022 and the Seemingly Unrelated Regression method, this paper sought to determine the relationship between green and non-green cryptocurrency indices as representative of blockchain technologies on three green bond indices in two periods before the outbreak of COVID-19 and the corona period. The preliminary results confirmed that by spreading the coronavirus, the sensitivity of the green bond market has increased to the gold index. In addition, the green cryptocurrency index (Cardano) significantly impacts green bonds more during the Corona era. The negative effect of the Bitcoin index on the index of green bonds in the Corona era is less than its adverse effect in the pre-corona period. Developing green cryptocurrencies and linking cryptocurrencies and digital green financing are two significant recommended policy implications for scholars and policymakers.
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This paper performs the asymmetric multifractal cross-correlation analysis to examine the COVID-19 effects on three relevant high-frequency fiat currencies, namely euro (EUR), yen (YEN) and the Great Britain pound (GBP), and two cryptocurrencies with the highest market capitalization and traded volume (Bitcoin and Ethereum) considering two periods (Pre-COVID-19 and during COVID-19). For both periods, we find that all pairs of these financial assets are characterized by overall persistent cross-correlation behavior (αxy(0) > 0.5). Moreover, COVID-19 promoted an increase in the multifractal spectrum's width, which implies an increase in the complexity for all pairs considered here. We also studied the Generalized Cross-correlation Exponent, which allows us to verify that there is no asymmetric behavior between Bitcoin and fiat currencies and between Ethereum and fiat currencies. We conclude that investing simultaneously in major fiat currencies and leading cryptocurrencies can reduce the portfolio risk, leading to improvement in the investment results.
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We investigate connectedness between energy cryptocurrencies and common asset classes, including oil, using TVP-VAR modeling, evidencing that energy cryptocurrencies, as diversifiers, normally have strong connections with bitcoin and nothing else. However, their connectedness to other assets changes rapidly during shocks such as COVID-19 and the start of the Russian-Ukraine war. Connectedness spiked in April 2020, when WTI oil prices fell to negative pricing. Economic policy uncertainty, Twitter-based uncertainty, and infectious disease-related uncertainty all have significant impact on the system's total connectedness. Energy cryptocurrencies, while normally diversifiers, are highly sensitive to shocks and changes in uncertainty.
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This study examines the performance of Islamic gold-backed cryptocurrencies during the bear market of 2020. Price data is collected for three Islamic gold-backed cryptocurrencies, OneGram, HelloGold and X8X, as well as the conventional gold-backed cryptocurrency, PaxGold, and the conventional fiat-backed cryptocurrency, Bitcoin, from December 2019 to November 2020. Analysis via ARMA-EGARCH models show that returns on all cryptocurrencies are lower during the bear market but the decrease is only significant for the Islamic gold-backed cryptocurrencies. Volatility is found to be higher for all five cryptocurrencies but the effect of the bear market on the volatility is only significant for the conventional cryptocurrencies.
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In this paper, we test the role of news in the predictability of return volatility of digital currency market during the COVID-19 pandemic. We use hourly data for cryptocurrencies and daily data for the news indicator, thus, the GARCH MIDAS framework which allows for mixed data frequencies is adopted. We validate the presupposition that fear-induced news triggered by the COVID-19 pandemic increases the return volatilities of the cryptocurrencies compared with the period before the pandemic. We also establish that the predictive model that incorporates the news effects forecasts the return volatility better than the benchmark (historical average)model.
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PurposeThe COVID-19 pandemic is known to have affected the logistics and supply chains;however, there is no adequate empirical evidence to prove in which way it has affected the relationship between the stocks related to this field with the corresponding cryptocurrencies. This paper aims to test the dynamic relationship of cryptocurrencies with supply chain and logistics stocks.Design/methodology/approachIn this paper, the author tests the causal and long-run relationship between logistics and supply chain stocks with the corresponding cryptocurrencies related to these fields, or those that are known to exhibit characteristics that can be utilized by these fields, testing also whether the COVID-19 pandemic affected this relationship. To do so, the author performs the variable-lag causality to test the causal relationship, and examines if this relationship changed due to COVID-19. The author then implements the multifractal detrended cross-correlation analysis to investigate the characteristics of a possible long-run relationship, testing also whether they changed due to COVID-19.FindingsThe results indicate that there is a positive long-run relationship between each logistics and supply chain stocks and the corresponding cryptocurrencies, before and also during COVID-19, but during COVID-19 this relationship becomes weaker, in most cases. Moreover, before COVID-19, the majority of the cases indicate a causal direction from cryptocurrencies to the stocks, while during COVID-19, the causal relationships decrease in multitude, and most cases unveil a causal direction from the stocks to cryptocurrencies.Originality/valueThe causal pattern changed during COVID-19, and the long-run relationship became weaker, showing a change in the dynamics in the relationship between logistics and supply chain stocks with cryptocurrencies.
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This paper examines the efficiency and asymmetric multifractal features of NFTs, DeFi, cryptocurrencies, and traditional assets using Asymmetric Multifractal Cross-Correlations Analysis covering the period from November 2017 to February 2022. Considering the full sample with a significant variation among asset classes, the study reveals DeFi-DigiByte is the most efficient while the cryptocurrency-Tether is the least efficient. However, S&P 500 showed high efficiency before COVID-19, and DeFi-Enjin Coin advanced as the most efficient asset during COVID-19. The volatility dynamics of NFTs, DeFi, and cryptocurrencies follow strong nonlinear cross-correlations, but evidence of weaker nonlinearity exists in traditional assets. Additionally, the sensitivity to smaller events in bull markets is high for NFTs and DeFi. The findings have significant implications for portfolio diversification when an investor's portfolio set includes traditional assets and cryptocurrency and relatively new blockchain-based assets like NFTs and DeFi. © 2023 The Authors
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PurposeThe purpose of this paper is to explore the relationship between Dow Jones Islamic Market World Index, Islamic gold-backed cryptocurrencies and halal chain in the presence of state (regime) dynamics.Design/methodology/approachThe authors have used the Markov-switching model to identify bull and bear market regimes. Moreover, the dynamic conditional correlation, the Baba, Engle, Kraft and Kroner- generalized autoregressive conditional heteroskedasticity and the wavelet coherence models are applied to detect the presence of spillover and contagion effects.FindingsThe findings indicate various patterns of spillover between halal chain, Dow Jones Islamic Market World Index and Islamic gold-backed cryptocurrencies in high and low volatility regimes, especially during the COVID-19 pandemic. Indeed, the contagion dynamics depend on the bull or bear periods of markets.Practical implicationsThese present empirical findings are important for current and potential traders in gold-backed cryptocurrencies in that they facilitate a better understanding of this new type of assets. Indeed, halal chain is a safe haven asset that should be combined with Islamic gold-backed cryptocurrencies for better performance in portfolio optimization and hedging, mainly during the COVID-19 period.Originality/valueTo the best of the authors' knowledge, this paper is the first research on the impact of the halal chain on the Dow Jones Islamic Market World Index return, Islamic gold-backed cryptocurrencies returns in the bear and bull markets around the global crisis caused by the COVID-19 pandemic.
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Virtual digital assets including cryptocurrencies, non-fungible tokens and decentralized financial asset have been initially used as an alternative currency but are currently being purchased as an asset and hedging instruments. Exponentially growing trading volume witnesses the growing inclination of investors towards these assets, and this calls for volatility analysis of these assets. In this reference, the present study assessed and compared the volatility of returns from investment in virtual digital assets, equity and commodity market. Daily closing prices of selected cryptocurrencies, non-fungible tokens and decentralized financial assets, stock indices and commodities have been analysed forthe post-covid period. Since returns were observed to be heteroscedastic, autoregressive conditional heteroscedastic models have been used to assess the volatility. The results indicate a low correlation of commodity investment with all other investment opportunities. Also, Tether and Dai have been observed to be negatively correlated with stock market. This indicates the possibility of minimizing risk through portfolio diversification. In terms of average returns, virtual digital assets are discerned to be better options than equity stock or commodity yet the variance scenario of these investment avenues is not very rosy. The volatility parameters reveal that unlike commodity market, virtual digital assets have got a significant impact of external shocks in the short-run. Further, the long run persistency of shocks is observed to be higher for the UK stock market, followed by Ethereum, Tether and Dai. The present analysis is crucial as the decision about its acceptance as legal tender money is still sub-judice in some countries. The results are expected to provide insight to regulatory bodies about these assets. © Author(s) 2022.
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This study examines the volatility changes of 20 cryptocurrencies from January 2018 to May 2021 using sparse VHAR-MGARCH model. Our proposed model incorporates the high-dimensionality and time-varying conditional heterogeneity of cryptocurrency markets. We examined the time-varying spillover index, dynamic correlation structure, and connectivity between cryptocurrencies. Our empirical analysis clearly shows that there was a volatility shift on 13 March 2020, due to a market crash caused by COVID-19. This naturally divides the data into three periods: pre-crisis, during the crisis, and post-crisis regimes. The pre-crisis regime exhibited long-term cyclic fluctuations in the spillover index. However, after the market crash, the spillover index remained at a very high level with almost no interconnections between cryptocurrencies. The post-crisis regime showed quite a few irregular and sharp spikes in the spillover index, together with record-breaking prices and volumes.
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Purpose: The purpose of this paper is to study the interlinkages between the cryptocurrency and stock market by characterizing their connectedness starting from January 1, 2018 to December 31, 2021. Design/methodology/approach: The author employs a time-varying parameter vector autoregression (TVP-VAR) in combination with an extended joint connectedness approach. Findings: The pandemic shocks appear to have influences on the system-wide dynamic connectedness, which reaches a peak during the COVID-19 pandemic. Net total directional connectedness suggests that each cryptocurrency and stock have a heterogeneous role, conditional on their internal characteristics and external shocks. In particular, Bitcoin and Binance Coin are reported as the net receiver of shocks, while the role of Ethereum shifts from receivers to transmitters. As for the stock market, the US stock market stays persistent as net transmitters of shocks, while the Asian stock market (including Hong Kong and Shanghai) are the two consistent net receivers. During the COVID-19 pandemic shock, pairwise connectedness reveals that cryptocurrencies can explain the volatility of the stock markets with the impact most severe at the beginning of 2020. Practical implications: Insightful knowledge about key antecedents of contagion among these markets also help policymakers design adequate policies to reduce these markets' vulnerabilities and minimize the spread of risk or uncertainty across these markets. Originality/value: The author is the first to investigate the interlinkages between the cryptocurrency and the stock market and assess the influences of uncertain events like the COVID-19 health crisis on the dynamic interlinkages among these two markets. The author employs the TVP-VAR combined with an extended joint connectedness approach. [ FROM AUTHOR] Copyright of Journal of Economic Studies is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Using the TYDL causality test, this paper attempts (i) to investigate the existence of shift contagion among a large spectrum of financial markets during recent stress and stress-free periods and (ii) to propose a new approach of portfolio management based on the minimization of the causal intensity. During the COVID-19 crisis period, the shift contagion analysis not only reveal a tripling of the causal links between the markets studied, but also a change in the causal structure. Beyond the initial impact of the COVID-19 crisis on financial markets, policy interventions seem to have helped in reassuring market participants that the further spread of financial stress would be mitigated. However, the Russian-Ukrainian conflict, and the high degree of uncertainty it entailed, has again exacerbated the interdependencies between financial markets. In terms of portfolio analysis, our minimum-causal-intensity approach records a lower (respectively higher) reward-to-volatility ratio than the Markowitz (1952 & 1959) minimum-variance traditional approach during the pre-COVID-19 (respectively pre-war) period. On the other hand, both approaches, the one we propose in this paper and the minimum-variance approach, record negative reward-to-volatility ratios during crisis periods.
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PurposeThe mining process is essential in cryptocurrency networks. However, it consumes considerable electrical energy, which is undoubtedly harmful to the environment. In response, energy-conserving cryptocurrency projects with reduced energy requirements or based on renewable energies have been developed. Recently, the COVID-19 pandemic and the Russian invasion of Ukraine ignited an unprecedented upheaval in financial products, especially in cryptocurrency and energy markets. Therefore, the paper aims to explore the response of these energy-conserving cryptocurrencies to the COVID-19 pandemic and the Russia–Ukraine conflict.Design/methodology/approachThis paper investigates the response of these energy-conserving cryptocurrencies to the COVID-19 pandemic and the Russia–Ukraine conflict. Their competitiveness is compared with conventional ones by analyzing their efficiency through multifractal detrended fluctuation analysis and automatic variance ratio during the COVID-19 and Russian invasion periods.FindingsThe empirical results show that all investigated energy-conserving cryptocurrencies negatively responded to the pandemic and positively reacted to the Russian invasion. On the other hand, all conventional cryptocurrencies reacted negatively to the COVID-19 pandemic and the amid-Russian attack. Besides, Bitcoin and SolarCoin were the least inefficient before the outbreak of COVID-19. Nevertheless, the Ethereum market became the most efficient after the pandemic spread. Similarly, the efficiency of Ripple was the most significant during the conflict between Russia and Ukraine. The energy crisis caused by Russia benefited the efficiency of the studied energy-conserving cryptocurrencies.Practical implicationsThis research is of interest to investors seeking opportunities in these energy-conserving cryptocurrencies and policymakers working to implement reforms to improve their market efficiency and promote long-term financial market growth.Originality/valueTo the best of the authors' knowledge, the behavior of cryptocurrencies based on renewable and reduced energy during the recent conflict between Russia and Ukraine has not been explored.
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We employ alpha-stable distribution to dynamically compute risk exposure measures for the five most traded cryptocurrencies. Returns are jointly modeled with an ARMA-GARCH approach for their conditional mean and variance processes with alpha-stable innovations. We use the MLE method to estimate the parameters of this distribution, along with those of conditional mean and variance. Our results show that the dynamic approach is superior to the static method. We also find out that these risk measures of five cryptocurrencies do not offer the same pattern of behavior across subperiods (i.e., pre-, during- and post-COVID pandemic). © 2023 Elsevier Inc.
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The phenomenon of cryptocurrencies still requires systematic and in-depth scientific research, because the literature lacks a concentrate and systematic analysis on issue of private, decentralized digital money (cryptocurrencies) in relation to the future of traditional money, as well as the stability of the financial system. Moreover, the is lack of research based on the opinions of participants of the financial system, which includes users – current and potential – as subjects relevant to the future of the financial system, based on the historically well-known principle of universal acceptance and trust in traditional monetary system. In particular, comparative and international research has received limited attention. In response to the identified research gap, this article refers to the results of research on the perception of cryptocurrencies by young financial market participants. We try to answer to the following research questions: 1) are there international differences in perceptions and attitudes toward the traditional monetary system and cryptocurrencies? 2) are cryptocurrencies constructed on the basis of blockchain a valid alternative to current fiat money? 3) do cryptocurrencies have characteristics that make them successors to fiat money? Our work is based on the research material collected during surveys conducted before the outbreak of the COVID-19 pandemic – in December 2019 and January 2020 – in Germany and Poland. The survey was conducted among 281 respondents – 143 from Germany and 128 from Poland. They were students of economics and finance majors of studies. The survey was conducted in the form of an auditorium questionnaire. The paper questionnaire used in the survey consisted of 26 questions related to virtual money and 5 questions of socio-demographic characteristics. In addition, the article used statistical methods – correlation and variance analysis – to characterize the distributions of responses and the relationships between questions. Our findings lead to the conclusion that there are significant differences in perception, the traditional monetary system, and cryptocurrencies due to a variety of factors, which include the level of development of the economy, the innovation of financial markets, historical warranty and being their derivative the so-called collective consciousness (mentality). The obtained research results can be a starting point for further in-depth analysis of the studied phenomenon at the international level, not only in the sphere of economics and finance, but also behavioral finance, sociology and psychology. © 2022 Narodowy Bank Polski. All rights reserved.
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The spectacular nature of bitcoin price crashes baffles market spectators and prompts routine warnings from regulators cautioning that cryptocurrencies behave in contra to the fundamental properties that traditionally define what constitutes money. Arguably most concerning to the public is, first, bitcoin's unprecedented price volatility relative to other asset classes and, second, its seemingly detached price behavior relative to time-honored economic and market fundamentals. In an attempt to create an early warning system of bitcoin price crash risk using generalized extreme value (GEV) and logistic regression modeling, this study integrates order flow imbalance, along with several control factors which reflect blockchain activity and network value, in order to nowcast bitcoin's price crashes. From a data analysis perspective, and despite their dissimilar distributional underpinnings, the GEV and logistic models perform comparably. When evaluating the type I and type II errors which these models yield, it is shown that their performance is comparable in terms of accuracy. In addition, it is also shown how the proportion of type I and type II errors can shift dramatically across probability cutoff tolerances. Towards the end of this study, time varying probabilities of a price crash are shown and evaluated. The sample range in this study encompasses the SARS-CoV-2 (Covid-19) time period as well as the recent scandal and collapse of the FTX cryptocurrency exchange. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.