Abstract The annual energy consumption of cryptocurrencies has been increasing in recent years. This paper studies the cryptocurrencies return volatility spillover and the underlying dynamics of five cryptocurrencies, namely Bitcoin, Bitcoin Cash, Ethereum, Ripple XRP and Litecoin's impact on four energy markets, namely Nifty Energy Index, S&P 500 Energy Index, S&P/TSX Canadian Energy Index and Shanghai Stock Exchange Energy Index for the period 2016–2021. We employed the Granger Causality Test and DCC MGARCH model to investigate the integration between cryptocurrencies and the energy markets. From the empirical analyses, we find that the overall time‐varying correlation between cryptocurrencies and the energy markets is low and weak. This study may be helpful for investors, academia and policymakers.
Abstract This study investigates the dynamic mechanism of financial markets on volatility spillovers across eight major cryptocurrency returns, namely Bitcoin, Ethereum, Stellar, Ripple, Tether, Cardano, Litecoin, and Eos from November 17, 2019, to January 25, 2021. The study captures the financial behavior of investors during the COVID-19 pandemic as a result of national lockdowns and slowdown of production. Three different methods, namely, EGARCH, DCC-GARCH, and wavelet, are used to understand whether cryptocurrency markets have been exposed to extreme volatility. While GARCH family models provide information about asset returns at given time scales, wavelets capture that information across different frequencies without losing inputs from the time horizon. The overall results show that three cryptocurrency markets (i.e., Bitcoin, Ethereum, and Litecoin) are highly volatile and mutually dependent over the sample period. This result means that any kind of shock in one market leads investors to act in the same direction in the other market and thus indirectly causes volatility spillovers in those markets. The results also imply that the volatility spillover across cryptocurrency markets was more influential in the second lockdown that started at the beginning of November 2020. Finally, to calculate the financial risk, two methods—namely, value-at-risk (VaR) and conditional value-at-risk (CVaR)—are used, along with two additional stock indices (the Shanghai Composite Index and S&P 500). Regardless of the confidence level investigated, the selected crypto assets, with the exception of the USDT were found to have substantially greater downside risk than SSE and S&P 500.
This paper presents how volatility propagates through the cryptocurrency market. Our paper provides evidence for volatility connectedness on cryptocurrencies. The different econometric techniques, including the stochastic volatility (SVOL) model and time-varying parameter VAR models using a quasi-Bayesian local likelihood (QBLL), are applied to measure the volatility of the cryptocurrency market. Using high-frequency, intra-day data of the largest cryptocurrencies over 2018–2021, we detect the great volatility of the cryptocurrency market are the beginning of 2019, the beginning of 2020, and throughout the year of 2021. The total connectedness values suggest that the cryptocurrency market becomes volatile as the new strains of the COVID-19 appear at the end of 2021. However, by using directional connectedness, we reveal that there are negative and positive spillovers from a specific cryptocurrency to other cryptocurrencies. The great fluctuations in the period before the COVID-19 health crisis stem from the positive resonance (symmetric) between the volatility of each cryptocurrency, while this health crisis leads to substantially positive and negative spillovers (asymmetric) of cryptocurrencies, and this makes market volatility weaker than it actually is.
In the past few years, along with the crypto assets market, a new term has appeared: stablecoins. Unlike cryptocurrencies, however, not so much research has been devoted to this topic. The emergence of global stablecoin projects, a significant increase in the volume of investment initiatives, and growth in the number of transactions have forced central banks to seriously pay attention to these in order to ensure financial stability as one of their functions. This topic is undoubtedly relevant due to the novelty of the concept which has appeared. The purpose of this article is to study the economic essence of stablecoins, their types, and the current state of this market. The methods of comparative analysis as well as critical and systematic approach to the study of information are used in the work. Existing ways to define the concept of stablecoins are investigated. The classifications of stablecoins and the main types of the most reliable coins on the market are examined. The current state of the stablecoin market is analyzed. As a result of the study, a number of conclusions have been made. Despite the lack of a legally fixed and generally accepted definition of stablecoins, in general, stablecoins are tokens secured by different types of assets. The economic essence of stablecoins is revealed through the goals of their creation, types of security and stabilization mechanisms, as well as the nature of the relationship between the issuer and the owner of the stablecoin. Over the past three years, the stablecoin market has grown almost fivefold. Such growth means significant penetration into the payment system, and then into the global financial system, which requires the development of international regulatory standards to minimize possible risks and preserve financial stability. The prospects for the development of stablecoins are associated with the creation and promotion of digital currencies of central banks (central securities) and cross-border payments in one or more central securities.
This study contributes to the COVID-19 related literature in finance by examining asymmetric volatility spillover across stock, Bitcoin, gold and oil markets before and during the COVID-19 pandemic. Based on multivariate VAR asymmetric BEKK GARCH model, findings show that the interdependency across the examined markets intensified during the recent health crisis. Moreover, we find that oil market appears as major receivers of volatility spillovers, particularly from gold and stock market which is mostly the results of dramatic collapse of oil prices during the COVID-19 outbreak. We also document that gold exhibits a strong resilience during COVID-19 crisis, suggesting its potential hedging ability during uncertainty. As for asymmetric volatility spillover, findings show the highest sensitivity of oil and Bitcoin markets to gold and US stock markets. Our findings have important implications for investors, portfolio managers and policymakers.
Abstract Beginning from the onset of the Covid-19 pandemic, crypto assets have intensely been in the spotlight and have attracted significant investor attention. By being the first blockchain product, Bitcoin is the first crypto asset and still dominates the entire crypto market capitalization. In this study, we shed light on whether this energy-hungry crypto asset is an effective tool for portfolio volatility reduction from the perspective of the Modern Portfolio Theory. Based on a two-year period from April 2019 to April 2021, which includes the extreme impacts (crash and rally) of the pandemic on markets, we conclude that Bitcoin is not a beneficial instrument for volatility reduction if short-selling is not allowed. After removing this restriction, Bitcoin has very small negative investment weights in minimum variance portfolios. In other words, short-selling Bitcoin slightly reduces portfolio volatility.
The uncertainty due to the COVID-19 outbreak has encouraged investors to look for value hedging instruments to minimize risk, which can be in the form of hedging assets or safe-haven assets. In response to it, this study aims to find out whether Bitcoin, Ethereum, and gold can behave as hedging and safe-haven assets before and amid the pandemic in Indonesia. The strategy is by observing the effects of volatility and return of Bitcoin, Ethereum, and gold on the Indonesian stock market. This study employed both quantile regression and simple linear regression models on data of daily closing price taken before and during COVID-19. This study finds that they can be hedge and safe-haven assets during the COVID-19 pandemic in Indonesia. The findings show some significant correlations between assets that can help investors determine which assets can be hedging instruments.
This paper investigates the causality and cointegration relationships between seven major cryptocurrencies, namely Bitcoin (BTC), Binance Coin (BNB), Cardano (ADA), Dogecoin (DOGE), Ethereum (ETH), Polkadot (DOT) and Ripple (XRP), using Johansen Cointegration and Granger Causality tests over the period from August 21, 2020 to April 19, 2021. Results indicate that there exists cointegration among cryptocurrencies in the long run. Findings also show that there is a bi-directional causal relationship between BNB and ETH. Additionally, BNB appears to be Granger cause of ADA, DOGE and DOT. On the other hand, analyses provide evidence of one-way causality running from XRP to both DOGE and DOT. These results might have some important implications for investors in terms of portfolio management.
Purpose This paper investigates the dynamic intercorrelation among cryptocurrency (Bitcoin) and conventional financial assets (gold, oil and S&P 500). Design/methodology/approach The dynamic contemporaneous nexus has been analyzed using spillover index developed and extended by Diebold and Yilmaz (2012, 2014) and Kyrtsou-Labys (2006) nonlinear causality tests. This study is implemented using the daily data spanning from January 2013 to December 2021. Findings First, using the spillover index, the authors find evidence that the S&P 500 was a net transmitter of volatility from oil and gold markets, but a net receiver of volatility from Bitcoin. Return spillovers from crude oil were transmitted first to gold, and Bitcoin markets and return spillovers from gold were transmitted to Bitcoin. Second, Kyrtsou-Labys nonlinear causality tests provide us further insights into the lead-lag interconnections among the four key considered variables from the economic perspective. Specifically, a close inspection of these empirical results, the integration of the four key assets is significant. Similarly, price fluctuation dependency among Bitcoin, stock, gold and oil markets is generally minimal, but it strengthens throughout the COVID-19 period. Originality/value This paper is the first study employing the spillover index Diebold-Yilmaz alongside with Kyrtsou-Labys nonlinear causality tests not only to capture the directional return spillover effects but also to highlight the potential presence of asymmetric causality relationships, nonlinear effects among assets under investigation that the previous studies have been ignored in these relations. Therefore, the main contribution of this article to the related literature in this field is significant.
Purpose The study considers time-varying risk premium in investigating the capability of technical analysis (TA) to predict and outperform a buy–hold strategy in Bitcoin exchange rate returns. Design/methodology/approach The study tests the technical trading rule of fixed moving average (FMA) on daily actual and equilibrium returns of Bitcoin exchange rates. The equilibrium returns are computed using dynamic CAPM in conjunction with a VAR-MGARCH (1, 1) system. The empirical evaluation of the study uses a case study of four Bitcoin exchange rates (BTC/AUD, BTC/EUR, BTC/JPY and BTC/ZAR) for the period 19 June 2010 to 30 October 2020. Findings The findings are consistent with related studies in conventional foreign exchange markets that find TA to be profitable, especially in emerging markets. Nevertheless, the consideration of risk premium has the effect of reducing the abnormal returns. Also, further robust tests reveal that Bitcoin returns possess a momentum effect which prompts further study in efficient market hypothesis research. Practical implications The empirical findings of this study should benefit portfolio managers and active investors on the strength of TA to predict returns in a speculative market like the Bitcoin exchange rate market. Originality/value The study takes cognisance that cryptocurrency trading is speculative in nature which renders it a good candidate for TA methods. While there are studies that have explored the value of TA in Bitcoin exchange rates, these studies fail to incorporate the effects of time-varying risk premiums, the strength and focus of the current paper.
In the decade following the 2008 financial crisis, the coronavirus viral disease 2019 (COVID-19) pandemic and United States (US) President Trump’s Twitter account became representations of market uncertainty, attracting the financial research of (Goodell, 2020; Benton and Philips, 2020). Due to the popularity of these events and their impact on financial markets, many unanswered questions still persist, particularly, how the financial structure has changed during this unique time. The popularity of Bitcoin, one of the main cryptocurrencies, has caused a controversial topic to arise in recent academic research, namely, whether its function compares to that of conventional precious metals such as gold and platinum. This doctoral thesis aims to fill this research gap in two ways: (i) by addressing market reactions to the COVID-19 pandemic and political news by answering the question of how US legislators traded at an industry level during the ongoing COVID-19 pandemic, and how Trump’s Twitter account could shake the equity market during a trade war, and (ii) by examining the power of the gold and platinum ratio, which was first studied by (Huang and Kilic, 2019) ), in predicting Bitcoin as well as how political sentiment could drive the returns, volatility, and volume of this cryptocurrency. This thesis contributes to the empirical evidence in the areas mentioned above due to the growing attention on the financial function of cryptocurrency, the debatable effects of political news regarding the use of social media, and the eventual and unprecedented scale of the COVID-19 pandemic.
Abstract 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.
Bitcoin is the world's first decentralized digital crypto currency which does not need an intermediary like a bank and is most secure because of block chain implementation. The price of a single bitcoin has been increasing drastically since 2010 as a form of digital gold. Thus, bitcoin is very volatile as its price changes every second which is a high risk for investors. The purpose of this paper is to analyse the machine learning algorithms which are of maximum efficiency in predicting the bitcoin price. I have explored many machine learning regression-based algorithms to build a prediction model for analysing future bitcoin prices. This paper is based on a deep learning-based artificial neural network model named GRU (Gated Recurrent Unit) to predict bitcoin future prices accurately based on past price information available. Root Mean Square Error and Mean Absolute Percent Error are the key performance indicators to measure forecast accuracy.
The goals of this paper are twofold: (1) to present a new method that is able to find linear laws governing the time evolution of Markov chains and (2) to apply this method for anomaly detection in Bitcoin prices. To accomplish these goals, first, the linear laws of Markov chains are derived by using the time embedding of their (categorical) autocorrelation function. Then, a binary series is generated from the first difference of Bitcoin exchange rate (against the United States Dollar). Finally, the minimum number of parameters describing the linear laws of this series is identified through stepped time windows. Based on the results, linear laws typically became more complex (containing an additional third parameter that indicates hidden Markov property) in two periods: before the crash of cryptocurrency markets inducted by the COVID-19 pandemic (12 March 2020), and before the record-breaking surge in the price of Bitcoin (Q4 2020 - Q1 2021). In addition, the locally high values of this third parameter are often related to short-term price peaks, which suggests price manipulation.
This paper explores how fear sentiment affects the price of Bitcoin by employing the rolling-window Granger causality tests. The analysis reveals negative influences from the volatility index (VIX) to Bitcoin price (BTC), which ascertains that Bitcoin can not be considered a haven in fear sentiment. Due to the liquidity in economic downside risks, BTC may decrease with high VIX to hedge losses, increasing during low VIX periods. The empirical results conflict with the intertemporal capital asset pricing model, which underlines that the increasing VIX can promote the price of Bitcoin. In turn, BTC positively impacts VIX, which shows that Bitcoin price can be treated as the main indicator for a more comprehensive analysis of the fear index. Under severe global uncertainty and changeable fluctuation of market sentiment, investors can optimize investment decisions based on market fear sentiment. The government can also consider VIX to grasp the trend of BTC to participate in cryptocurrency speculation effectively.
Abstract This paper analyses the return and realized volatility spillovers among Bitcoin, wilder hill clean energy index (ECO), S&P 500 as conventional stocks and West Texas Intermediate (WTI) from 11/11/2013 to 30/09/2021. We investigate the transmission mechanism with Time-Varying Parameter Vector Auto regression (TVP-VAR). Our findings indicate that stock markets such as clean energy and conventional transmit return shocks to Bitcoin and oil and receive volatility shocks from Bitcoin and oil. In addition, during non-crisis periods, Bitcoin and other financial markets are weakly related; but, during crisis periods, such as the great cryptocurrency crash in 2018 and the coronavirus pandemic in 2020, their connection increases significantly.