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Purpose: This study aims to provide empirical evidence on the return and volatility spillover structures between Bitcoin, Fintech stocks and Asian-Pacific equity markets over time and during different market conditions, and their implications for portfolio management. Design/methodology/approach: We use Time-varying parameter vector autoregressive and quantile frequency connectedness approach models for the connectedness framework, in conjunction with Diebold and Yilmaz's connectivity approach. Additionally, we use the minimum connectedness portfolio model to highlight implications for portfolio management. Findings: Regarding the uncertainty of the whole system, we show a small contribution from Bitcoin and Fintech, with a higher contribution from the four Asian Tigers (Taiwan, Singapore, Hong Kong and Thailand). The quantile and frequency analyses also demonstrate that the link among assets is symmetric, with short-term spillovers having the largest influence. Finally, Bitcoins and Fintech stocks are excellent diversification and hedging instruments for Asian equity investors. Practical implications: There is an instantaneous, symmetric and dynamic return and volatility spillover between Asian stock markets, Fintech and Bitcoin. This conclusion should be considered by investors and portfolio managers when creating risk diversification strategies, as well as by policymakers when implementing their financial stability policies. Originality/value: The study's major contribution is to analyze the volatility spillover between Bitcoin, Fintech and Asian stock markets, which is dynamic, symmetric and immediate. [ABSTRACT FROM AUTHOR]

Predicting Bitcoin prices is crucial because they reflect trends in the overall cryptocurrency market. Owing to the market's short history and high price volatility, previous research has focused on the factors influencing Bitcoin price fluctuations. Although previous studies used sentiment analysis or diversified input features, this study's novelty lies in its utilization of data classified into more than five major categories. Moreover, the use of data spanning more than 2,000 days adds novelty to this study. With this extensive dataset, the authors aimed to predict Bitcoin prices across various timeframes using time series analysis. The authors incorporated a broad spectrum of inputs, including technical indicators, sentiment analysis from social media, news sources, and Google Trends. In addition, this study integrated macroeconomic indicators, on-chain Bitcoin transaction details, and traditional financial asset data. The primary objective was to evaluate extensive machine learning and deep learning frameworks for time series prediction, determine optimal window sizes, and enhance Bitcoin price prediction accuracy by leveraging diverse input features. Consequently, employing the bidirectional long short-term memory (Bi-LSTM) yielded significant results even without excluding the COVID-19 outbreak as a black swan outlier. Specifically, using a window size of 3, Bi-LSTM achieved a root mean squared error of 0.01824, mean absolute error of 0.01213, mean absolute percentage error of 2.97%, and an R-squared value of 0.98791. Additionally, to ascertain the importance of input features, gradient importance was examined to identify which variables specifically influenced prediction results. Ablation test was also conducted to validate the effectiveness and validity of input features. The proposed methodology provides a varied examination of the factors influencing price formation, helping investors make informed decisions regarding Bitcoin-related investments, and enabling policymakers to legislate considering these factors. [ABSTRACT FROM AUTHOR]

This study investigates the interconnectedness between cryptocurrency and the Indian stock market and explores the diversification, hedge, and safe haven potential of cryptocurrency. The study employs the wavelet approach on daily data from October 6, 2017, to October 5, 2022, to execute the empirical analysis. The findings confirm that, in a healthy economic environment, cryptocurrencies are not connected with the Indian stock market. However, during times of financial turmoil, Bitcoin, Ethereum, and Cardano are positively correlated with the stock market. Additionally, the study identifies Bitcoin, Ethereum, Dogecoin, and Cardano as competent in providing diversification, or hedge opportunities in normal economic situations. But during periods of financial stress, only Dogecoin may act as a safe haven asset. This is the first study to explore the time-varying correlations and causal dependencies between the stock and cryptocurrency markets in India using the wavelet approach. It extends the literature in finance by examining both normal and economic turmoil periods, providing insights for portfolio managers, policymakers, and investors on how to manage their portfolios during these periods. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research is to primarily understand how crypto traders can use the Bitcoin as a hedge or safe haven asset to reduce their losses from crypto trading. The study also aims to provide insights to crypto investors (portfolio managers) who wish to maintain a crypto portfolio for the medium term and can use the Bitcoin to minimize their losses. The findings of this research can also be used by policymakers and regulators for accommodating the Bitcoin as a medium of exchange, considering its safe haven nature. Design/methodology/approach: This study applies the cross-quantilogram (CQ) approach introduced by Han et al. (2016) to examine the safe-haven property of the Bitcoin against the other selected crypto assets. This method is robust for estimating bivariate volatility spillover between two markets given unusual distributions and extreme observations. The CQ method is capable of calculating the magnitude of the shock from one market to another under different quantiles. Additionally, this method is suitable for fat-tailed distributions. Finally, the method allows anticipating long lags to evaluate the strength of the relationship between two variables in terms of durations and directions simultaneously. Findings: The Bitcoin acts as a weak safe haven asset for a majority of new crypto assets for the entire study period. These results hold even during greed and fear sentiments in the crypto market. The Bitcoin has the ability to protect crypto assets from sharp downturns in the crypto market and hence gives crypto traders some respite when trading in a highly volatile asset class. Originality/value: This study is the first attempt to show how the Bitcoin can act as a true matriarch/patriarch for crypto assets and protect them during market turmoil. This study presents a clear and concise representation of this relationship via heatmaps constructed from CQ analysis, depicting the quantile dependence association between the Bitcoin and other crypto assets. The uniqueness of this study also lies in the fact that it assesses the protective properties of the Bitcoin not only for the entire sample period but also specifically during periods of greed and fear in the crypto market. [ABSTRACT FROM AUTHOR]

This study employs a fixed-effects model to investigate the holiday effect in the cryptocurrency market, using trading data for the top 100 cryptocurrencies by market capitalization on Coinmarketcap.com from January 1, 2017 to July 1, 2022. The results indicate that returns on cryptocurrencies increase significantly during Chinese holiday periods. Additionally, we use textual analysis to construct an investor sentiment indicator and find that positive investor sentiment boosts cryptocurrency market returns. However, when positive investor sentiment prevails in the cryptocurrency market, the holiday effect weakens, implying that positive investor sentiment attenuates the holiday effect. Robustness tests based on the Bitcoin market generate consistent results. Moreover, this study explores the mechanisms underlying the cryptocurrency holiday effect and examines the impact of epidemic transmission risk and heterogeneity characteristics on this phenomenon. These findings offer novel insights into the impact of Chinese statutory holidays on the cryptocurrency market and illuminate the role of investor sentiment in this market. [ABSTRACT FROM AUTHOR]

A number of financial bubbles have occurred throughout history. The objective of this study was to identify the main similarities between Bitcoin price behavior during bubble periods and a number of historical bubbles. Once this had been carried out, we aimed to determine whether the solutions adopted in the past would be effective in the present to reduce investors' risk in this digital asset. This study brings a new approach, as studies have previously been conducted analyzing the similarity of Bitcoin bubbles to other bubbles individually, but these were not conducted in such a broad manner, addressing different types of bubbles, and over such a broad time period. Starting from a dataset with 9967 records, a combined methodology was used. This consisted of an analysis of the standard deviations, the growth rates of the prices of the assets involved, the percentage increase in asset prices from the origin of the bubble to its peak and its fundamental value, and, finally, the bubble index. Lastly, correlation statistical analysis was performed. The results obtained from the combination of the above methods reveal the existence of certain similarities between the Bitcoin bubbles (2011, 2013, 2017, and 2021) and the tulip bubble (1634–1637) and the Mississippi bubble (1719–1720). We find that the vast majority of the measures taken to avoid past bubbles will not be effective now; this is due to the digital and decentralized nature of Bitcoin. A limitation of the study is the difficulty in making a comparison between bubbles that occurred at different historical points in time. However, the results obtained shed light and provide guidance on the actions to be taken by regulators to ensure the protection of investors in this digital asset. [ABSTRACT FROM AUTHOR]

This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional (Bitcoin and Ethereum) and Islamic (Stellar and Ripple) cryptocurrencies. Using a novel time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach combined with a high frequency (hourly) dataset ranging from 1st June 2018 to 22nd July 2022, we find that (i) good and bad spillovers are time-varying; (ii) bad volatility spillovers are more pronounced than good spillovers; (iii) a strong asymmetry in the volatility spillovers exists in the cryptocurrency market; and (iv) conventional cryptocurrencies dominate Islamic cryptocurrencies. Specifically, Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers. [ABSTRACT FROM AUTHOR]

Cryptocurrency is the most advanced technology and financial product and the focus of attention of investors pursuing safe-haven assets. This study evaluates the asymmetric multifractality of the cryptocurrencies in the different phases of the coronavirus disease (COVID-19) through a novel approach of asymmetric multifractality detrended fluctuation analysis. The study is a valuable contribution by analyzing the multifractality in the downward and upward price movements. The outcomes show that the major cryptocurrencies have multifractals, which increase as the fractal scale increases. The higher multifractality is observed in the downward (upward) movements for Bitcoin and Ripple, suggesting that both are less efficient and weaker safe havens against unpredictability during the pandemic and throughout the period. The multifractality is greater in the upside (downside) for Ethereum and the market efficiency improves during the pandemic and the full period, as indicated by the greater upward deviation than the downward. The results explore that cryptocurrencies are inefficient during the pandemic. Ethereum requires special attention to monitor such sudden changes in the price dynamic and regulations are extremely critical. This will help investors to develop portfolios to minimize risk and forecast future price trends. [ABSTRACT FROM AUTHOR]

This study aims to identify the factors that robustly contribute to Bitcoin liquidity, employing a rich range of potential determinants that represent unique characteristics of the cryptocurrency industry, investor attention, macroeconomic fundamentals, and global stress and uncertainty. To construct liquidity metrics, we compile 60-min high-frequency data on the low, high, opening, and closing exchange rates of Bitcoin against the US dollar. Our empirical investigation is based on the extreme bounds analysis (EBA), which can resolve model uncertainty issues. The results of Leamer's version of the EBA suggest that the realized volatility of Bitcoin is the sole variable relevant to explaining liquidity. With the Sala-i-Martin's variant of EBA, however, four more variables, (viz. Bitcoin's negative returns, trading volume, hash rates, and Google search volume) are also labeled as robust determinants. Accordingly, our evidence confirms that Bitcoin-specific factors and developments, rather than global macroeconomic and financial variables, matter for explaining its liquidity. The findings are largely insensitive to our proxy of liquidity and to the estimation method used. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the dynamic conditional correlation (DCC) and hedging ratios between Dow Jones markets and oil, gold and bitcoin. Using daily data, including the COVID-19 pandemic and the Russia–Ukraine war. We employ the DCC-generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric DCC (ADCC)-GARCH models. Design/methodology/approach: DCC-GARCH and ADCC-GARCH models. Findings: The most of DCCs among market pairs are positive during COVID-19 period, implying the existence of volatility spillovers (Contagion-effects). This implies the lack of additional economic gains of diversification. So, COVID-19 represents a systematic risk that resists diversification. However, during the Russia–Ukraine war the DCCs are negative for most pairs that include Oil and Gold, implying investors may benefit from portfolio-diversification. Our hedging analysis carries significant implications for investors seeking higher returns while hedging their Dow Jones portfolios: keeping their portfolios unhedged is better than hedging them. This is because Islamic stocks have the ability to mitigate risks. Originality/value: Our paper may make a valuable contribution to the existing literature by examining the hedging of financial assets, including both conventional and Islamic assets, during periods of stability and crisis, such as the COVID-19 pandemic and the Russia–Ukraine war. [ABSTRACT FROM AUTHOR]

As the crypto-asset ecosystem matures, the use of high-frequency data has become increasingly common in decentralized finance literature. Using bibliometric analysis, we characterize the existing cryptocurrency literature that employs high-frequency data. We highlighted the most influential authors, articles, and journals based on 189 articles from the Scopus database from 2015 to 2022. This approach enables us to identify emerging trends and research hotspots with the aid of co-citation and cartographic analyses. It shows knowledge expansion through authors' collaboration in cryptocurrency research with co-authorship analysis. We identify four major streams of research: (i) return prediction and measurement of cryptocurrency volatility, (ii) (in)efficiency of cryptocurrencies, (iii) price dynamics and bubbles in cryptocurrencies, and (iv) the diversification, safe haven, and hedging properties of Bitcoin. We conclude that highly traded cryptocurrencies' investment features and economic outcomes are analyzed predominantly on a tick-by-tick basis. This study also provides recommendations for future studies. [ABSTRACT FROM AUTHOR]

This study uses high-frequency (1-min) price data to examine the connectedness among the leading cryptocurrencies (i.e. Bitcoin, Ethereum, Binance, Cardano, Litecoin, and Ripple) at volatility and high-order (third and fourth orders in this paper) moments based on skewness and kurtosis. The sample period is from February 10, 2020, to August 20, 2022, which captures a pandemic, wartime, cryptocurrency market crashes, and the full collapse of a stablecoin. Using a time-varying parameter vector autoregressive (TVP-VAR) connectedness approach, we find that the total dynamic connectedness throughout all realized estimators grows with the time frequency of the data. Moreover, all estimators are time dependent and affected by significant events. As an exception, the Russia–Ukraine War did not increase the total connectedness among cryptocurrencies. Analysis of third- and fourth-order moments reveals additional dynamics not captured by the second moments, highlighting the importance of analyzing higher moments when studying systematic crash and fat-tail risks in the cryptocurrency market. Additional tests show that rolling-window-based VAR models do not reveal these patterns. Regarding the directional risk transmissions, Binance was a consistent net transmitter in all three connectedness systems and it dominated the volatility connectedness network. In contrast, skewness and kurtosis connectedness networks were dominated by Litecoin and Bitcoin and Ripple were net shock receivers in all three networks. These findings are expected to serve as a guide for portfolio optimization, risk management, and policy-making practices. [ABSTRACT FROM AUTHOR]

Cryptocurrencies have been historically characterised by large price swings and inherent volatility at a much higher scale than traditional financial assets. Understanding the underlying mechanisms and whether, or how, these are priced in through possible risk premia is crucial to bringing cryptocurrencies closer to mainstream financial markets. Using data on 1982 cryptocurrencies form January 1, 2015 till September 30, 2020 and a combination of models involving portfolio-level and Fama–MacBeth analyses, while accounting for cryptocurrency sample selection, we show that the additional risk measured by idiosyncratic volatility is well priced in cryptocurrencies and investors are being paid a risk premium for their holdings. However, a deeper inspection of the dynamics reveals that such a trade-off is mostly valid for the most illiquid cryptocurrencies, which are susceptible to microstructure noise. [ABSTRACT FROM AUTHOR]

COVID-19 has significantly influenced global financial markets, including Bitcoin. Recent studies have focused on investigating the first wave of the COVID-19 outbreak and accounting for market changes, which were mostly due to the pandemic. This research not only analyzes the contagion effects of COVID-19 but also considers aftermath events beyond the first pandemic wave to examine spillovers of Bitcoin. The study employs Diebold and Yilmaz's method to explore the static and dynamic spillovers of the selected variables and identifies several major global events, including crypto-specific affairs, macroeconomic policies, and geopolitical conflicts, to explain the new market dynamics of Bitcoin using network analysis. The findings identify a few high-contagion periods related to Bitcoin. The paper also found that Bitcoin is more likely to produce extreme returns and is more connected to other markets. Contagion effects "from" and "to" other markets are asymmetrical in terms of arrival time and market response. Bitcoin is more likely to be affected by other markets in extreme situations and receives spillovers from them sooner than it transmits spillovers to others. In the context of various global events, impacts arising from developed countries are stronger. China still has some impact on cryptocurrency markets, but they are waning. Bitcoin is thus not a safe haven from the shocks of global events, but can sometimes work as a hedge or diversifier. The results offer alternative explanations for Bitcoin's different market dynamics and enrich our understanding of Bitcoin's safe haven, hedge, and diversifier properties within a diversified portfolio. [ABSTRACT FROM AUTHOR]

The Bitcoin market has experienced unprecedented growth, attracting financial traders seeking to capitalize on its potential. As the most widely recognized digital currency, Bitcoin holds a crucial position in the global financial landscape, shaping the overall cryptocurrency ecosystem and driving innovation in financial technology. Despite the use of technical analysis and machine learning, devising successful Bitcoin trading strategies remains a challenge. Recently, deep reinforcement learning algorithms have shown promise in tackling complex problems, including profitable trading strategy development. However, existing studies have not adequately addressed the simultaneous consideration of three critical factors: gaining high profits, lowering the level of risk, and maintaining a high number of active trades. In this study, we propose a multi-level deep Q-network (M-DQN) that leverages historical Bitcoin price data and Twitter sentiment analysis. In addition, an innovative preprocessing pipeline is introduced to extract valuable insights from the data, which are then input into the M-DQN model. A novel reward function is further developed to encourage the M-DQN model to focus on these three factors, thereby filling the gap left by previous studies. By integrating the proposed preprocessing technique with the novel reward function and DQN, we aim to optimize trading decisions in the Bitcoin market. In the experiments, this integration led to a noteworthy 29.93% increase in investment value from the initial amount and a Sharpe Ratio in excess of 2.7 in measuring risk-adjusted return. This performance significantly surpasses that of the state-of-the-art studies aiming to develop an efficient Bitcoin trading strategy. Therefore, the proposed method makes a valuable contribution to the field of Bitcoin trading and financial technology. [ABSTRACT FROM AUTHOR]

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The aim of this study is to determine the number of transactions among the currencies, which will eventually become a part of our lives, cannot be physically held, can move quickly, and emerge as a new shopping and investment tool in the changing world order, as of the year (2023) when this study was conducted. The study focuses on the analysis of the variables that affect the most popular currency, Bitcoin. Although the analysis of variables that influence Bitcoin was determined as the primary aim of the study, the study also attempted to reach a general conclusion about the variables affected by the cryptocurrencies. Since there is no other cryptocurrency that is traded as much as Bitcoin, Bitcoin is thought to be a good model for the analysis of cryptocurrencies. The method used in the study was autoregressive conditional heteroskedastic (ARCH) models. It is believed that the most suitable models for the Bitcoin variable, whose value changes every second, are ARCH and its derivatives. Other models selected from the ARCH models were also added to the analysis as a method. The models used in the study can be listed as follows: linear ARC, generalized ARC (GARCH), exponential GARCH and threshold GARCH. A statistical model called autoregressive conditional heteroscedasticity (ARCH) is used to study the volatility of time series. Through the provision of a volatility model that more closely mimics actual markets, ARCH modeling is utilized in the financial sector to quantify risk. According to ARCH modeling, periods of high volatility are followed by even higher volatility, and periods of low volatility are followed by even lower volatility. In this study, 5 different variables were selected using literature to analyze the variables affecting Bitcoin returns using ARCH models. The dependent variable in the study is the price of Bitcoin. The remaining variables were included in the models as independent variables. These variables are actually variables that are accepted and selected as the best among a set of variables. In other words, 15 variables were first added to the study using the literature. After this, a correlation analysis was carried out. As a result of the correlation analysis, the variables with the highest correlation with the price of Bitcoin, which is the dependent variable, and the lowest correlation with each other were retained in the model. These variables are Bitcoin Price, Crude Oil Spot Price, Euro-Dollar Parity, Gold Spot Price and NASDAQ Composite Index. The study period is between 2020 and 2023 and it was studied using daily data. Days with no data were removed from the daily period from 2020 to 2023 and loss of information was prevented. After removing missing observations, this study examined the remaining 837 observations. During the research, while running the models created using different methods, it was found that the model that gives the best result is the GARCH model. In other words, when modeling the variables affecting bitcoin (cryptocurrency from the perspective of the population), it was seen that the GARCH model gave the best results when comparing linear ARCH, generalized ARCH (GARCH), exponential GARCH, and threshold GARCH of the ARCH model.Comparing the output of the GARCH model with other ARCH models not included in this study can be a recommendation for the future study [ABSTRACT FROM AUTHOR]

This study contributes to the unconsolidated cryptocurrency literature, with a systematic literature review focused on cryptocurrency market microstructure. We searched Web of Science database and focused only on journals listed on 2021 ABS list. Our final sample comprises 138 research papers. We employed a quantitative and an integrative analysis, and revealed complex network associations, and a detailed research trending analysis. Our study provides a robust and systematic contribution to cryptocurrency literature by making use of a powerful and accurate methodology—the bibliographic coupling, also by only considering ABS academic journals, using a wider keyword scope, and not enforcing any restrictions regarding areas of knowledge, thus enhancing the contribution of extant literature by allowing the insights of more high-quality peripheral studies on the subject. The conclusions of this study are of extreme importance for researchers, investors, regulators, and the academic community in general. Our study provides high structured networking and clear information for research outlets and literature strands, for future studies on cryptocurrency investment, it also presents valuable insights to better understand the cryptocurrency market microstructure and deliver helpful information for regulators to effectively regulate cryptocurrencies. [ABSTRACT FROM AUTHOR]

This study measures the convergence and divergence of major cryptocurrencies by applying two distance measures used in machine learning. Particularly, the time-varying Euclidean distance measure was constructed by combining the first four moments (i.e. mean, variance, skewness and kurtosis) of the return distributions of cryptocurrencies following the ℓ 2 -normalisation. It was found that major cryptocurrencies converged to the centroid during the 2018 market crash, but diverged before and after the crash. Their divergence could be due to the uncertainty arising from market news and regulatory events. In addition, Bitcoin cosine similarity measure was developed to provide further insights into the relationship between Bitcoin and other cryptocurrencies. This cosine similarity shows how each cryptocurrency moves relative to Bitcoin, which is not captured by the Euclidean distance. More importantly, it was demonstrated that the divergence of major cryptocurrencies from their centroids can improve Markowitz's efficient frontier and provide more diversification benefits to investors and portfolio fund managers. Finally, a profitable trading strategy was provided based on the Euclidean distance. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the conditional volatility of the Asian stock market concerning Bitcoin and global crude oil price movement. Design/methodology/approach: This study uses the newest Dynamic Conditional Correlation (DCC)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to examine the conditional volatility of the stock market for Bitcoin and crude oil prices in the Asian perspective. The sample stock market includes Chinese, Indian, Japanese, Malaysian, Pakistani, Singaporean, South Korean and Turkish stock exchanges, with daily time series data ranging from 4 April 2015−31 July 2023. Findings: The outcome reveals the presence of volatility clustering on the return series of crude oil, Bitcoin and all selected stock exchanges of the current study. Secondly, the outcome of DCC, manifests that there is no short-run volatility spillover from crude oil to the Malaysian, Pakistani and South Korean and Turkish stock markets, whereas Chinese, Indian, Japanese, Singapore stock exchanges show the short-run volatility spillover from crude oil in the short run. On the other hand, in the long run, there is a volatility spillover effect from crude oil to all the stock exchanges. Thirdly, the findings suggest that there is no immediate spillover of volatility from Bitcoin to the stock markets return volatility of China, India, Malaysia, Pakistan, South Korea and Singapore. In contrast, both the Japanese and Turkish stock exchanges exhibit a short-term volatility spillover from Bitcoin. In the long term, a volatility spillover effect from Bitcoin is observed in all stock exchanges except for Malaysia. Lastly, based on the outcome of conditional variance, it can be concluded that there was increase in the return volatility of stock exchanges during the period of the COVID-19 pandemic. Research limitations/implications: The analysis below does not account for the bias induced due to certain small sample properties of DCC-GARCH model. There exists a huge literature that suggests other methodologies for small sample corrections such as the DCC connectedness approach. On the other hand, decisive corollaries of the conclusions drawn above have been made purely based on a comprehensive investigation of eight Asian stock exchange economies. However, there is scope for inclusive examination by considering other Nordic and Western financial markets with panel data approach to get more robust inferences about the reality. Originality/value: Most of the empirical analysis in this perspective skewed towards the Nordic and Western countries. In addition to that many empirical investigations examine either the impact of crude oil price movement or Bitcoin performance on the stock market return volatility. However, none of the examinations quests the crude oil and Bitcoin together to unearth their implication on the stock market return volatility in a single study, especially in the Asian context. Hence, current investigation endeavours to examine the ramifications of Bitcoin and crude oil price movement on the stock market return volatility from an Asian perspective, which has significant implications for the investors of the Asian financial market. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the paper is to investigate the risk-hedging and/or safe haven properties of environmental, social and governance (ESG) index during the COVID-19 in China. Design/methodology/approach: This paper employs the DCC, VCC, CCC as well as Newey–West estimator regression. Findings: The findings provide empirical evidence of the risk hedging properties of ESG indexes as well as of the environmental, social and governance thematic indexes during the outbreak of the COVID-19 crisis. The results also support the superior risk hedging properties of ESG indexes over cryptocurrency. However, the authors do not find any safe haven properties of ESG, Bitcoin, gold and West Texas Intermediate (WTI). Practical implications: The paper offers therefore, practical policy implications for asset managers, central bankers and investors suggesting the pandemic risk-hedging opportunities of ESG investments. Originality/value: The study represents one of the first empirical contributions examining safe-haven and hedging properties of ESG indexes compared to traditional and innovative safe haven assets, during the eruption of the COVID-19 crisis. [ABSTRACT FROM AUTHOR]

Purpose: This article aims to uncover the impact of Google Trends on cryptocurrency markets beyond Bitcoin during the time of increased attention to altcoins, especially during the COVID-19 pandemic. Design/methodology/approach: This paper analyses the nexus among the Google Trends and six cryptocurrencies, namely Bitcoin, New Economy Movement (NEM), Dash, Ethereum, Ripple and Litecoin by utilizing the causality-in-quantiles technique on data comprised of the years January 2016–March 2021. Findings: The findings show that Google Trends cause the Litecoin, Bitcoin, Ripple, Ethereum and NEM prices at majority of the quantiles except for Dash. Originality/value: The findings will help investors to develop more in-depth understanding of impact of Google Trends on cryptocurrency prices and build successful trading strategies in a more matured digital assets ecosystem. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to analyze the connectedness between bitcoin (BTC) and other traditional assets (e.g. metals) in times of financial turbulence like the COVID pandemic. The purpose is to see to what extent BTC is mimicking the role precious metals are known for, that of being a reliable store of value. Design/methodology/approach: The author relies on vector autoregressive modeling, as it yields a very flexible framework for forecasting and interpreting the interdependencies among variables, while providing a very intuitive framework when the underlying structural model is unknown. The author performs the analysis first for the whole sample and then for a "COVID-19 subsample." Findings: The author finds evidence supporting a stronger link between BTC and gold in COVID-19 times, with BTC the main driving force. The author quantifies the contribution of BTC to the surge in gold's price during those early months of COVID-19 in the order of 28%, thus lending support to the idea of intrinsic or fundamental value in BTC. Practical implications: Investors might consider including BTC in their portfolios as a long-term investment, very much like they do with gold. Social implications: Cryptos at large represent an important check on the ever-expanding monetary policy pursued by central banks in recent times. Originality/value: The COVID-19 pandemic has taken its toll on the world economy. It is thus timely to reevaluate the relationship between BTC and other assets such as gold and silver, which are traditionally seen as safe havens against uncertainty. [ABSTRACT FROM AUTHOR]

Purpose: Crypto-asset can be traded on many different exchanges worldwide with servers located in countries with different financial characteristics and institutional surroundings. Trading volume on these servers varies considerably regarding the server's location, even though the prices do not differ greatly. Crypto-asset markets are poorly regulated and, as such, may leave a place for potential fraudulent activities and be linked to corruption. This paper aims to examine the role of country's institutions in attracting Bitcoin traders. Design/methodology/approach: Assuming heterogeneity between countries where crypto-asset exchange servers are located, the Pool Mean Group Estimator is used. Findings: Results indicate that, from institutional variables, corruption in the country attracts while internal and external conflicts repel investors. Additionally, the growth of global uncertainty and the decline in the local stock markets motivate investors to trade Bitcoin. Originality/value: Previous research has empirically proved the importance of institutions' quality for financial market development. This paper goes one step further and tries to empirically confirm the theoretical assumptions and investigate in detail the role of institutions in choosing servers in a particular country for Bitcoin trading. [ABSTRACT FROM AUTHOR]

The emergence of the covid-19 health crisis, in this advanced technological era where connections between markets, nations, and economies have grown stronger than ever before, the shock of the COVID-19 pandemic quickly had an impact on both physical and digital financial assets. The Chinese financial market experienced the first consequences of the covid-19 pandemic, then spilled over to other financial markets, including those for cryptocurrencies and the precious metals. This study examines the impact of the covid-19 pandemic on the volatilities of the dynamics of bitcoin and gold. Both assets share some characteristics, such as online trading platforms, however, gold is a tangible financial asset unlike bitcoin, which is digitally generated without any physical form. This study argues that the similarities and differences between bitcoin and gold play major roles in how the covid-19 crisis affected their respective dynamics. Using daily data ranging from 9/22/2014 to 1/31/2023 and employing ARMA as the mean equation for GARCH model, the impact of the health crisis (covid-19) is examined on the volatilities of the prices and volumes of bitcoin and gold. Empirical evidence points out that, the pandemic has a symmetric impact on the volatilities of bitcoin and gold price returns, causing them to be more volatile. The impact of the covid-19 observed on the volume returns of the assets, however, is asymmetrical. The empirical results give evidence to the role that the vital differences existing between these assets played during the covid-19 pandemic. [ABSTRACT FROM AUTHOR]

Based on traditional market theory, this study aims to investigate whether conventional market investment slopes affect the unconventional Bitcoin market, considering both normal conditions and crises. This study examines three main characteristics of the economy-intensive blockchain system, namely reliability, investment slopes, financial and accounting aspects that ultimately determine the confidence in the choice to invest in cryptocurrency. The analysis focuses on the study of the Bitcoin (BTC) investment slopes during January 2014–April 2023, considering the specifics of blockchain technology and the inferences of ethics, reliability and real-world data on investment Tassets in the context of conventional regulated markets. Using an econometric model that incorporates reliability analysis techniques, factorial comparisons and multinomial regression using economic crisis periods as a dummy variable, this study reveals important findings for practical and academic purposes. The results of this study show that the investment slopes of Bitcoin (BTC) are mostly predictable for downward trends, when statistically significant correlations with the investment slopes of conventional stock markets are observable. The moderate or high increase in performance slopes pose several challenges for predictive analysis, as they are influenced by other factors than conventional regulated market performance inferences. The results of this study are of intense interest to researchers and investors alike, as they demonstrate that investment slopes analysis sheds light on the intricacies of investment decisions, allowing a comprehensive assessment of both conventional markets and Bitcoin transactions. [ABSTRACT FROM AUTHOR]

With Bitcoin being universally recognized as the most popular cryptocurrency, more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. As a result, many transactions can encounter different confirmation delays. Concerned about this, it becomes vital to help a user understand (if possible) how long it may take for a transaction to be confirmed in the Bitcoin blockchain. In this work, we address the issue of predicting confirmation time within a block interval rather than pinpointing a specific timestamp. After dividing the future into a set of block intervals (i.e., classes), the prediction of a transaction's confirmation is treated as a classification problem. To solve it, we propose a framework, Hybrid Confirmation Time Estimation Network (Hybrid-CTEN), based on neural networks and XGBoost to predict transaction confirmation time in the Bitcoin blockchain system using three different sources of information: historical transactions in the blockchain, unconfirmed transactions in the mempool, as well as the estimated transaction itself. Finally, experiments on real-world blockchain data demonstrate that, other than XGBoost excelling in the binary classification case (to predict whether a transaction will be confirmed in the next generated block), our proposed framework Hybrid-CTEN outperforms state-of-the-art methods on precision, recall and f1-score on all the multiclass classification cases (4-class, 6-class and 8-class) to predict in which future block interval a transaction will be confirmed. [ABSTRACT FROM AUTHOR]

This study investigates the extent to which intraday price jumps and co-jumps in cryptocurrency markets stem from the release of macroeconomic news from the U.S., Germany and Japan. Using 5-min frequency prices for Bitcoin and Ethereum quoted against the U.S. dollar over the 2016–2019 period, we find that intraday price jumps are three times more frequent in Ethereum than in Bitcoin. More importantly, we show that jumps in Ethereum are more sensitive to macroeconomic news than jumps in Bitcoin, and that U.S. news releases exhibit higher influence on jumps in both cryptocurrencies than German and Japanese news announcements. We also find that co-jumps among Bitcoin and Ethereum are scarce and tend be associated only with a few U.S. news announcements, and in particular those related to the unemployment rate, new home sales, housing starts and Fed Beige Book. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the hedge, safe-haven and diversifier properties of Islamic indexes, Bitcoin and gold for ten of the most affected countries by the coronavirus, which are the USA, Brazil, the UK, Italy, Spain, Germany, France, Russia, China and Malaysia. Design/methodology/approach: This research uses the Ratner and Chiu (2013) methodology based on the dynamic conditional correlation models to improve Baur and McDermott (2010). The authors adopt a careful investigation of the features of a diversifier, hedge and safe haven using the dynamic conditional correlation–GARCH and quantile regression models. Findings: Empirical results indicate that Islamic indexes are not considered as hedge assets for the conventional market for all studied countries during the COVID-19 pandemic crisis period. However, gold works as a strong hedge in all countries, except for Brazil and Malaysia. Bitcoin is a strong hedge in the USA and a strong hedge and safe haven in China. Practical implications: International investors in China and the US stock markets should replace Islamic ‎indexes with Bitcoin in their conventional portfolio of securities during the pandemic. Originality/value: To the best of the authors' knowledge, this is the first paper that re-evaluates the hedge, safe-haven and diversifier properties of Islamic indexes, Bitcoin and gold for ten of the most affected countries by the coronavirus. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to provide new perspectives on the integration of East Asian stock markets and the dynamic volatility transmission to the Bitcoin market utilising daily data from 2014 to 2020. Design/methodology/approach: The authors undertake comprehensive analyses of the dependency dynamics, systemic risk and volatility spillover between major East Asian stock and Bitcoin markets. The authors employ a vine-copula-CoVaR framework and a VAR-BEKK-GARCH method with a Wald test. Findings: (a) With exception of KS11 and N225; HSI and SSE; HSI and KS11, which have moderate dependence, dependencies among other markets are low. In terms of tail risk, the upper tail risk is more significant in capturing strong common variation. (b) Two-way and asymmetric risk spillover effects exist in all markets. The Hong Kong and Japanese stock markets have significant risk spillovers to other markets, and quite notably, the Chinese stock market is the largest recipient of systemic risk. However, the authors observe a more significant risk spillover from the Chinese stock market to the Bitcoin market. (c) The VAR-BEKK-GARCH results confirm that the Korean market is a significant emitter of volatility spillovers. The Bitcoin market does provide diversification benefits. Interestingly, the Chinese stock market has an intriguing relationship with Bitcoin. (d) An increase in spillovers in East Asia boosts spillovers to Bitcoin, but there is no intuitive effect of Bitcoin spillovers on East Asian spillovers. Originality/value: For the first time, the authors examine the dynamic linkage between Bitcoin and the major East Asian stock markets. [ABSTRACT FROM AUTHOR]

This paper studies time‐frequency connectedness among carbon assets, Bitcoin, and global stock markets by using the Diebold and Yilmaz method and the Baruník and Křehlík method, to investigate the hedging ability of carbon assets and Bitcoin in global stock markets. Our study finds that both carbon assets and Bitcoin play hedging roles in global stock markets. However, their strength of hedging is negatively correlated with the degree of economic uncertainty and tends to change in different frequency domains. We also show that carbon assets and Bitcoin can act as each other's hedging assets in a great majority of cases. Our results provide useful knowledge for investors to reduce risks and for regulators to regulate carbon assets and cryptocurrency speculation. [ABSTRACT FROM AUTHOR]

Purpose: This paper investigates the impact of global sentiment and various coronavirus disease 2019 (COVID-19)-related media coverage news (Media-Hype index; Panic Index; Media Coverage Index, infodemic index and coronavirus statistics) on the dynamics of bitcoin returns during the COVID-19 pandemic using an asymmetric framework. Design/methodology/approach: The authors use an asymmetric framework based on quantile regression (QR) and quantile-on-quantile regression. Findings: QR results show that COVID-19 panic news negatively affects bitcoin market returns at times of extreme bearish. However, COVID-19 bullish sentiment negatively impacts bitcoin market returns during bullish market conditions. Quantile-on-quantile approach's (QQA) empirical results show that the effects of COVID-19-related news on bitcoin returns were heterogeneous, mainly negative and varied across quantiles. Research limitations/implications: The authors find some significant differences regarding the impact of news on bitcoin return dynamics compared to stock markets, suggesting the safe-haven role of bitcoin against stock during the ongoing epidemic. Practical implications: The authors find some significant differences regarding the impact of news on bitcoin return dynamics compared to stock markets, suggesting the safe-haven role of bitcoin against stock during the ongoing epidemic. Originality/value: This study contributes to understanding the dynamics of bitcoin returns using various COVID-19 media news. [ABSTRACT FROM AUTHOR]

In this paper, we predict money laundering in Bitcoin transactions by leveraging a deep learning framework and incorporating more characteristics of Bitcoin transactions. We produced a dataset containing 46,045 Bitcoin transaction entities and 319,311 Bitcoin wallet addresses associated with them. We aggregated this information to form a heterogeneous graph dataset and propose three metapath representations around transaction entities, which enrich the characteristics of Bitcoin transactions. Then, we designed a metapath encoder and integrated it into a heterogeneous graph node embedding method. The experimental results indicate that our proposed framework significantly improves the accuracy of illicit Bitcoin transaction recognition compared with traditional methods. Therefore, our proposed framework is more conducive in detecting money laundering activities in Bitcoin transactions. [ABSTRACT FROM AUTHOR]

We present a textual analysis that explains how Elon Musk's sentiments in his Twitter content correlates with price and volatility in the Bitcoin market using the dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity model, allowing less sensitive to window size than traditional models. After examining 10,850 tweets containing 157,378 words posted from December 2017 to May 2021 and rigorously controlling other determinants, we found that the tone of the world's wealthiest person can drive the Bitcoin market, having a Granger causal relation with returns. In addition, Musk is likely to use positive words in his tweets, and reversal effects exist in the relationship between Bitcoin prices and the optimism presented by Tesla's CEO. However, we did not find evidence to support linkage between Musk's sentiments and Bitcoin volatility. Our results are also robust when using a different cryptocurrency, i.e., Ether this paper extends the existing literature about the mechanisms of social media content generated by influential accounts on the Bitcoin market. [ABSTRACT FROM AUTHOR]

This article aims at investigating the extent to which the algorithmic nature (i.e., mining process) of cryptocurrencies might influence their dynamics and interaction with some major economic indicators. Our study observes that proof‐of‐stake based cryptocurrencies are less correlated with other crypto‐assets offering more opportunities for diversifying portfolio strategy. We also observe a positive correlation between the proof‐of‐work based cryptocurrencies and the oil price. This article discusses these matters and suggests that the differences in cryptocurrencies' dynamics are more related to their service or purpose rather than their mining protocol. This claim contributes to the current debates on the intrinsic value of cryptocurrencies and it is illustrated with a discussion of the Stellar (XLM) and Ether (ETH) cases. Beyond our empirical results, our article suggests that, the liquidity and the returns dynamics of cryptocurrencies might be affected by two different aspects. Precisely, the former appears to be influenced by the economic service for which these cryptocurrencies are used, while cryptocurrencies' returns are more reactive to the way their cryptographic validation is operated. Our findings also suggest that an analysis through the economic service/purpose of cryptocurrencies is actually appropriate to understand their dynamics in relation to economic indicators. This perspective implicitly questions the monetary aspect often associated with cryptocurrencies and it calls for a more categorized research (by economic purpose) of cryptocurrencies whose potential intrinsic value would then be related to their economic purpose. [ABSTRACT FROM AUTHOR]

The paper seeks to determine whether Bitcoin behaves differently from forex markets and Gold, and whether it offers any diversification, hedging, or safe-haven potential. A Markov regime-switching regression model is employed to determine the relationship between Bitcoin, the real economic activity, foreign exchange markets, financial markets, Energy, and Gold. The results indicate that, unlike USD/EUR and Gold, besides other variables, Bitcoin exhibits significant deviations in terms of its association with other financial and economic variables. Bitcoin appears to be strikingly positively associated with equity markets in both regimes. This may limit its potential to either act as a hedge or a safe-haven for US Equity markets. Bitcoin also deviates considerably from Gold and USD/EUR as it is not affected by the same set of variables as Gold or USD/EUR are under either regime. Moreover, while Gold appears to offer considerably weak safe-haven properties, particularly against equity, Bitcoin fails to be a safe-haven for any of the assets under study. The results, however, indicate that the properties of Bitcoin may range between a diversifier and a hedge, however, such potential of Bitcoin must be viewed with caution owing to the large volatility exhibited by Bitcoin. [ABSTRACT FROM AUTHOR]

Academic research relies heavily on exogenous drivers to improve the forecasting accuracy of Bitcoin volatility. The present study provides additional insight into the role of both macroeconomic and technical indicators in forecasting the realized volatility of Bitcoin. Using 17 famous macroeconomic variables and 18 technical indicators between December 2011 and April 2021, the results reveal that the shrinkage methods, including elastic net and LASSO, can powerfully extract predictive information from macroeconomic and technical indicators. We further investigate the forecasting power of macroeconomic factors and technical indicators in terms of variable selection, business cycle, and volatility levels, and the results show strong evidence that the macroeconomic indicators (namely, S&P 500 realized volatility, global real economic activity index, and trade‐weighted USD index return) are the most frequently selected by shrinkage method, suggesting that their ability to forecast Bitcoin volatility is stronger than that of technical indicators. However, technical indicators are more powerful in forecasting Bitcoin volatility during the low volatility state. [ABSTRACT FROM AUTHOR]

Purpose: This paper investigates the causality among gold prices, crude oil prices, bitcoin and stock prices by using daily data from January 2014 to December 2021. The study also examines the data during the COVID-19 outbreak from January 2020 to December 2021. Design/methodology/approach: To estimate the long- and short-run causality, this study considers the nonlinear autoregressive distributed lag (NARDL) cointegration test. Findings: The analysis found the existence of an asymmetric long-run cointegration among selected assets. Findings indicate that positive changes in bitcoin do not affect stock market in the long term. Changes in crude oil prices have a significant impact on stock prices. Moreover, it is observed that variations in the stock prices trigger a negative impact on gold prices. During the COVID-19 period, the study notices the presence of an asymmetric long-term cointegration between selected assets except bitcoin. Besides, findings revealed that negative price adjustments in gold lead to significant positive shocks in stock market. Originality/value: These results provide critical information for policy performers and researchers to develop new strategies. Policy regulators can also consider the potential effects of the COVID-19 outbreak while developing strategies for investment decisions. [ABSTRACT FROM AUTHOR]

Motivation: So far, nothing has been known about the impact of the war in Ukraine on the dynamics of cryptocurrency markets, in particular on the intraday trading patterns in Bitcoin, nowadays the largest market capitalization and trading volume cryptocurrency. Aim: Use the data from the period January-April 2022 exhibiting over 1.5 million trades at Bitstamp, one out of four largest in trading volume Bitcoin markets, to identify and compare the day of the week and the hour of the day effects in the rate of return, bidask spread and trading volume at times preceding and following the war outbreak. Results: The analysis based on the regression including dummy variables showed that the bid-ask spread and trading volume exhibited both the day of the week and the hour of the day effects. The same applied to the rate of return -- but only incidentally. The effects differed in the magnitude across the peace and war periods being mostly smaller in the latter one due to increased risk faced by market participants who, as reflected in the number of trades and trading volume, lowered their activity. Since Bitcoin at Bitstamp is traded 24 hours a day and 7 days a week and incoming information is continuously impounded into its prices, the intraday trading patterns are different from those at the mature and emerging stock markets. The wider spreads and larger trading volumes were found present close to open of the major stock markets (NYSE, NASDAQ, London, Frankfurt, Tokyo). [ABSTRACT FROM AUTHOR]

Modern-day revolution is brought on by cryptocurrencies. Both the terms cryptocurrency & currency describe means of exchanging goods & services. In system that uses cryptocurrencies, every transaction is preprogrammed. Underlying blockchain technology governs transactions by using consensus mechanisms, hash algorithms, and public and private key encryption, doing away with the need for user to have faith in the counterparty. In this study, impact of macro-factors on bitcoin in Pakistan was examined. The relevance of this study is that the majority of people are ignorant of bitcoin to spread awareness of bitcoin and how to use them among the general audience. The macroeconomic indicators (GDP, INF, Exchange, & Gold) were monitored on a quarterly basis between JAN 2016 and DEC 2021. The E-view program and numerous tests on secondary data were also used in this inquiry. In Pakistan, there is no long-term link. Bitcoin's short-term relationships with Pakistan's GDP, INF, ER, and gold. In short term, there is a link between bitcoin and the exchange rate. [ABSTRACT FROM AUTHOR]

This study examines the asymmetric effects of global factors on the returns of cryptocurrencies. In this context, the study focuses on Bitcoin, Ethereum, and Ripple as top-traded cryptocurrencies, considers eight global factors, and uses high-frequency (i.e., daily) data between February 8, 2018, and June 21, 2022. Also, the study applies nonparametric causality in quantiles (NCQ) and quantile-on-quantile regression (QQR) as the main models and quantile regression (QR) as the robustness model. The empirical findings reveal that (i) effects of global factors on the returns of cryptocurrencies are asymmetric and generally positive; (ii) there are nonparametric causal effects from global factors to cryptocurrencies for return and volatility in most quantiles excluding some lower and higher quantiles; (iii) effects of the global factors on cryptocurrencies differentiate according to global factors, cryptocurrencies, and quantiles; (iv) the QR results confirm the robustness of the empirical findings; and (v) overall, the outcomes underline the asymmetric and varying effects of global factors on the returns of cryptocurrencies across quantiles. Hence, the results imply that traders and investors should consider the asymmetric effects of global factors on the returns of cryptocurrencies for trading, investment, and hedging purposes. Moreover, some policy recommendations are proposed. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic poses a serious threat to investors in the crude oil market. Furthermore, investors have an increasing need to find a safe haven in their investment portfolios when facing unprecedented risks in crude oil markets during the COVID-19 pandemic. According to a review of the literature, there are contradictory findings on which investment is the safer haven for the oil market. Therefore, this paper aims to evaluate whether bitcoin is a safer haven for the crude oil market than the commonly used gold during the COVID-19 pandemic. Three spillover measurements based on the time, and frequency domains, and a network framework are employed to quantify the return spillover effects among bitcoin, gold and three major crude oil futures markets. We divide the sample into two periods, pre-COVID-19 and post-COVID-19. The results show that bitcoin has a weak safe-haven effect on the crude oil market only over a short period, while gold maintains a good safe-haven ability for crude oil futures across various time horizons (frequencies), both before and after the outbreak of the COVID-19 pandemic. The findings of this study have important implications for policy-makers, crude oil producers and global investors. In particularly, investors cannot ignore the importance of bitcoin and gold in selecting more profitable portfolio policies when searching for safe-haven assets. [ABSTRACT FROM AUTHOR]

Anomalies, which are incompatible with the efficient market hypothesis and mean a deviation from normality, have attracted the attention of both financial investors and researchers. A salient research topic is the existence of anomalies in cryptocurrencies, which have a different financial structure from that of traditional financial markets. This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market, which is hard to predict. It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods. An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies. On October 6, 2021, Bitcoin (BTC), Ethereum (ETH), and Cardano (ADA), which are the top three cryptocurrencies in terms of market value, were selected for this study. The data for the analysis, consisting of the daily closing prices for BTC, ETH, and ADA, were obtained from the Coinmarket.com website from January 1, 2018 to May 31, 2022. The effectiveness of the established models was tested with mean squared error, root mean squared error, mean absolute error, and Theil's U1, and R OOS 2 was used for out-of-sample. The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models. When the models created with feedforward artificial neural networks are examined, the existence of the day-of-the-week anomaly is established for BTC, but no day-of-the-week anomaly for ETH and ADA was found. [ABSTRACT FROM AUTHOR]

In this paper we predict Bitcoin movements by utilizing a machine-learning framework. We compile a dataset of 24 potential explanatory variables that are often employed in the finance literature. Using daily data from 2nd of December 2014 to July 8th 2019, we build forecasting models that utilize past Bitcoin values, other cryptocurrencies, exchange rates and other macroeconomic variables. Our empirical results suggest that the traditional logistic regression model outperforms the linear support vector machine and the random forest algorithm, reaching an accuracy of 66%. Moreover, based on the results, we provide evidence that points to the rejection of weak form efficiency in the Bitcoin market. [ABSTRACT FROM AUTHOR]

This study explores the stylized facts, volatility clustering, other highly irregular behaviour, and risk measures of cryptocurrencies’ returns. By analysing bitcoin, ripple, and ethereum daily data we establish evidence of strong dependencies among analysed cryptocurrencies. This paper provides new insights about cryptocurrency behaviour and the main measures of risk and detailed comparative analysis with tech-stocks. Comprehensive research on stylized facts confirmed high risk for both cryptocurrencies and tech-stocks with cryptocurrencies being even riskier. Empirical research findings are useful in developing dependence and risk strategies for investment and hedging purposes, especially during more volatile periods in the markets as there was confirmed existence of volatility clusters when high volatility periods are followed by low volatility periods. Sensitivity analysis and measures of Value-at-Risk (VaR) and Expected Shortfall (ES) show the amount of losses investors can expect in the worst case scenario. Our results confirm the existence of predictability, volatility clustering, and possibilities for arbitrage opportunities. Findings could be beneficial for investors and policymakers as well as for scientific purposes as findings give us a better understanding of the behaviour of cryptocurrencies. [ABSTRACT FROM AUTHOR]

We present an analysis on variability Bitcoin characteristics that help to quantitatively differentiate Bitcoin from the state-owned traditional currencies and the asset Gold. We provide a detailed study on returns of exchange rates—against the Swiss Franc—of several traditional currencies together with Bitcoin and Gold; for that purpose, we define a distance between currencies by means of the spectral densities of the ARMA models of the returns of the exchange rates, and we present the computed matrix of the distances between the chosen currencies. A statistical analysis of these matrix distances is further proposed, which shows that the distance between Bitcoin and any other currency or Gold is not comparable to any of the distances between currencies or between currencies and Gold and not involving Bitcoin. This result shows that Bitcoin is essentially different from the traditional currencies and from Gold, at least in what concerns the structure of its variance and auto-covariances. [ABSTRACT FROM AUTHOR]

Purpose: Even though Bitcoin has been often labelled as a safe haven asset class in the literature, the influence of economic policy uncertainty (EPU) on the diversifying opportunities offered by Bitcoin in relation to other assets needs to be investigated. This paper aims to investigate how the EPU affects diversification of commodity, conventional, Islamic and sustainable equity returns in relation to its impact on Bitcoin returns. Design/methodology/approach: The authors use advanced time-series econometrics, namely, multivariate generalized autoregressive conditional heteroscedastic-dynamic conditional correlation and continuous wavelet transformation, for the analysis of the daily returns for the aforementioned assets between 01 August 2011 and 01 September 2019. Findings: First, the authors found a strong evidence of Bitcoin's mean reverting trend in the long run while its volatility has decreased significantly since 2013. After separating the EPU into two regimes (high and low), diversification opportunities with Bitcoin seems to disappear in a high EPU period, while the hedging opportunity tends to prevail in a low EPU period for all classes of assets. Importantly, the findings indicate that Bitcoin offers short-term diversification for sustainable and Islamic equity as well as energy stocks during a low uncertainty period. Consequently, in relation to the policy uncertainty, Bitcoin provides similar hedging opportunities than commodities like Gold and Silver. Overall, the study shows that EPU is remarkably important in explaining the average portfolio returns of Bitcoin, suggesting that this indicator can be perceived as a decent explanatory factor for portfolio diversification. Originality/value: The study significantly extends the empirical literature of Bitcoin's portfolio diversification by taking EPU into consideration. To the best of authors' knowledge, this is one of the few studies to investigate the asymmetric effects of US EPU on Bitcoin's hedging capabilities by taking into account major conventional equity, sustainable equity, Islamic equity, gold, silver and oil. [ABSTRACT FROM AUTHOR]

Purpose: This article unveils first the lead–lag structure between the confirmed cases of COVID-19 and financial markets, including the stock (DJI), cryptocurrency (Bitcoin) and commodities (crude oil, gold, copper and brent oil) compared to the financial stress index. Second, this paper assesses the role of Bitcoin as a hedge or diversifier by determining the efficient frontier with and without including Bitcoin before and during the COVID-19 pandemic. Design/methodology/approach: The authors examine the lead–lag relationship between COVID-19 and financial market returns compared to the financial stress index and between all markets returns using the thermal optimal path model. Moreover, the authors estimate the efficient frontier of the portfolio with and without Bitcoin using the Bayesian approach. Findings: Employing thermal optimal path model, the authors find that COVID-19 confirmed cases are leading returns prices of DJI, Bitcoin and crude oil, gold, copper and brent oil. Moreover, the authors find a strong lead–lag relationship between all financial market returns. By relying on the Bayesian approach, findings show when Bitcoin was included in the portfolio optimization before or during COVID-19 period; the Bayesian efficient frontier shifts to the left giving the investor a better risk return trade-off. Consequently, Bitcoin serves as a safe haven asset for the two sub-periods: pre-COVID-19 period and COVID-19 period. Practical implications: Based on the above research conclusions, investors can use the number of COVID-19 confirmed cases to predict financial market dynamics. Similarly, the work is helpful for decision-makers who search for portfolio diversification opportunities, especially during health crisis. In addition, the results support the fact that Bitcoin is a safe haven asset that should be combined with commodities and stocks for better performance in portfolio optimization and hedging before and during COVID-19 periods. Originality/value: This research thus adds value to the existing literature along four directions. First, the novelty of this study lies in the analysis of several financial markets (stock, cryptocurrencies and commodities)' response to different pandemics and epidemics events, financial crises and natural disasters (Correia et al., 2020; Ma et al., 2020). Second, to the best of the authors' knowledge, this is the first study that examine the lead–lag relationship between COVID-19 and financial markets compared to financial stress index by employing the Thermal Optimal Path method. Third, it is a first endeavor to analyze the lead–lag interplay between the financial markets within a thermal optimal path method that can provide useful insights for the spillover effect studies in all countries and regions around the world. To check the robustness of our findings, the authors have employed financial stress index compared to COVID-19 confirmed cases. Fourth, this study tests whether Bitcoin is a hedge or diversifier given this current pandemic situation using the Bayesian approach. [ABSTRACT FROM AUTHOR]

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Bitcoin's growing use as a financial asset and transaction instrument has economic and monetary effects. In this paper, we examine the short- and long-term interactions between Bitcoin prices and the money supply, consumer price index (CPI), and economic policy uncertainty (EPU) in the US. Using monthly data covering the period July 31, 2010 to August 31 2020, we employ continuous wavelet transforms, wavelet coherence, wavelet-based vector autoregressive Granger causality test, and nonlinear causality test. The results indicate that Bitcoin prices affect money supply and share dynamic inter-shock with CPI, EPU, and money supply. Specifically, the money supply and EPU negatively affect Bitcoin prices. CPI positively affects Bitcoin prices in the short-term, which supports the role of Bitcoin as a hedging asset. A bidirectional volatility transmission exists between Bitcoin prices and each of money supply, CPI, and EPU. Moreover, nonlinear causality test results show a bidirectional causality from Bitcoin price to money supply across all dimensions and a significant causality with CPI and EPU. The findings matter to investors seeking to refine their investment decisions while considering the effect of economic factors and to policymakers and central banks seeking to formulate policy tools using Bitcoin. [ABSTRACT FROM AUTHOR]