This paper examines the forecasting power of daily infectious disease-related uncertainty in predicting the realized volatility of nine foreign exchange futures and the Bitcoin futures series using the heterogeneous autoregressive realized variance model. Our results indicate that the infectious diseases-related uncertainty index plays a crucial role in predicting the future path of foreign exchange and Bitcoin futures realized volatility in all the selected time intervals. These findings have important implications for portfolio managers and investors during periods of high levels of uncertainty associated with infectious diseases.
With the control of the cryptocurrency market in environmental protection, investors pay attention to the risk conduction mechanism between energy consumption and the Bitcoin market. This paper applies quantile connectedness to analyse the overall situation and dynamic evolution of information spillover in the system of the Bitcoin market. The results show that the hashrate and electricity demand are the primary sources of risk in the information network, and their fluctuations have intensified the risk spillover effects in the system. In addition, the spillover level is more prominent in extreme cases, which means the information linkage in the system is integrated. The spillover effect of each variable fluctuates and is uncertain with time. This helps in the sustainable development of Bitcoin and guides the government's policy development and supervision of cryptocurrencies. The risk infection path helps prevent the risk of infection in the Bitcoin market and improves the sustainability of the encrypted market.
Despite the rapid growth of developing markets, aided by globalization, comparative studies of cryptocurrency and stock market volatility have focused on the developed markets and neglected developing ones. In this regard, this study compares cryptocurrency volatility with that of the Johannesburg Stock Exchange (JSE), a developing market. GARCH-type models are applied to daily log returns of Bitcoin, Ethereum, and the FTSE/JSE 4O in two ways. Firstly, the models are applied directly; secondly, structural breaks are tested and accounted for in the models. The sample period was from September 18, 2017, to May 27, 2021. The results show higher volatility and higher volatility persistence in cryptocurrency than in the JSE market. They also show that persistence is overestimated for cryptocurrencies when structural breaks are not accounted for. The opposite was true for the JSE.Moreover, the two cryptocurrencies were found to have close to identical volatility plots that differ from that of the JSE. High volatility periods of cryptocurrency also did not coincide with that of JSE and those of JSE did not coincide with the cryptocurrency ones. There is also evidence of an inverse leverage effect in cryptocurrency, which opposes the normal leverage effect of the JSE market.
Among the new way of exchanging money, using crypto currency has been very popular. Its also an investment to get good returns over the period of time. Cryptocurrency has grown to more than 120 million investors around the world as per a survey of 2021.Its growing at the 15 to 20% ratio around the world every year. This fact leads to a serious consideration of security and its vulnerabilities in block chain. Apart from market risks, high volatility, lack of rules and regulations, cyber risks are one of the most required types which needs proper attention and technical understanding. Because the crypto currencies are fully decentralized the risk of attacks is exposed and in most of the cases defenseless. Proof of stake and proof of work are two major algorithms followed by almost all crypto currencies to allot stocks to the holders. In this paper, different types of risks and attacks with POS and POW are explained with its mitigation. The problems and outcomes are examined, reviewed and conferred in case of Ethereum and Bitcoin crypto currencies. These currencies decentralized frameworks and anonymity attracts unlawful activities. Recognizing and preventing them needs understanding of the mechanism of attacks which are discussed in easiest possible ways for even a new-bee or an outsider person.
We examine the relationships among Bitcoin (BTC), the Chinese Yuan (CNY), and Chinese capital outflows between 2014-2021. We find that BTC returns strongly comove with CNY returns after 2018Q1, while no significant BTC/CNY relationship exists before 2018Q1. Further, the strength of the BTC/CNY relationship increases throughout 2018 to the present date. Yet, this relationship strength cannot be explained by periods of ascending BTC prices, changes in crypto mining location, nor changes in the use of BTC "mining pools". Instead, we find that the strength of the BTC/CNY relationship is strongly and directly related to Chinese capital outflows. We find no similar relationship with a "bogey" currency, the Euro, implying that the capital outflows -to- BTC/CNY relationship is unique to China and its capital outflow environment. In total, our novel results suggest that BTC is used as part of a process to move economically significant amounts of capital from mainland China.
Abstract This article investigates similarities and differences between gold and four cryptocurrencies (Bitcoin, Ethereum, Bitcoin Cash and Litecoin) with respect to four determinants. To do so, we estimate a system-GARCH-in-mean for the period starting 7/18/2014 at earliest until 7/12/2021. We find that, first, liquidity premia are almost always insignificant for both gold and cryptocurrencies. Second, volatility premia exist in either gold and cryptocurrencies. Third, the response of cryptocurrencies to exchange rate changes is more pronounced than for gold at least if developing countries are included. Fourth, gold exhibits a safe haven status, while cryptocurrencies do not. So according to our results those cannot be seen as a store of value but rather should be seen as speculative assets.
Mobeen Ur Rehman, Paraskevi Katsiampa, Rami Zeitun, Xuan Vinh Vo
This paper investigates the extreme dependence and risk spillovers between Bitcoin and the currencies of the BRICS and G7 economies. We find time-varying dependence between Bitcoin and all currencies. Moreover, when analysing risk spillovers from Bitcoin to currencies, we find that Bitcoin exercises significant power over most currencies, with the South African rand and Brazilian real holding both the highest downside and upside risk before and during the COVID-19 pandemic period, respectively. When considering risk spillovers from currencies towards Bitcoin, the Japanese yen exhibits the highest downside spillovers. Importantly, we find asymmetric spillovers between extreme upward and downward movements.
Seyram Pearl Kumah, Jones Odei‐Mensah, Richmell Baaba Amanamah
This paper investigates the co-movement between cryptocurrencies and African stock returns to uncover their degree of association and global portfolio diversification benefits implementing the three-dimensional continuous Morlet wavelet transform technique. Data span 10 August 2015 to 10 December 2021 at daily frequency. The results suggest high degrees of co-movement between the asset markets at medium and lower frequencies implying that stock markets in Africa are highly exposed to cryptocurrency market disruptions from the medium term and that international investors seeking to hedge their price risk in African stock markets using cryptocurrencies may have to look at the short term. The phase difference arrow vectors implying lead (lag) effects are time-varying and heterogeneous showing no particular cryptocurrency or stock market as leader or follower. Different markets have the potential to lead or lag other markets at varying scales which may induce arbitrage opportunities for international and local investors. Our findings provide insights for policymakers, regulators and international investors as an economy’s monetary policy can be affected by the connections between the domestic capital market and other markets globally.
Yizhi Wang, Florian Horky, Lennart John Baals, Brian M. Lucey · 5 authors
Amid surging market values and widespread regulatory discussion, NFT and DeFi markets are widely perceived as being simply speculative in nature. This paper detects the existence and dates of price bubbles in the NFT and DeFi markets by applying SADF and GSADF tests. We document that NFT and DeFi markets both exhibit speculative bubbles, with NFT bubbles being more recurrent and having higher average explosive magnitudes than DeFi bubbles. The price bubbles in the NFT and DeFi markets are highly correlated with market hype and with more general cryptocurrency market uncertainty. We do find periods where bubbles are not detected, suggesting that these markets do have some intrinsic value and should not be dismissed as simply bubbles.
This paper aims to empirically examine long memory and bi-directional information flow between estimated volatilities of highly volatile time series datasets of five cryptocurrencies. We propose the employment of Garman and Klass (GK), Parkinson's, Rogers and Satchell (RS), and Garman and Klass-Yang and Zhang (GK-YZ), and Open-High-Low-Close (OHLC) volatility estimators to estimate cryptocurrencies' volatilities. The study applies methods such as mutual information, transfer entropy (TE), effective transfer entropy (ETE), and Rényi transfer entropy (RTE) to quantify the information flow between estimated volatilities. Additionally, Hurst exponent computations examine the existence of long memory in log returns and OHLC volatilities based on simple R/S, corrected R/S, empirical, corrected empirical, and theoretical methods. Our results confirm the long-run dependence and non-linear behavior of all cryptocurrency's log returns and volatilities. In our analysis, TE and ETE estimates are statistically significant for all OHLC estimates. We report the highest information flow from BTC to LTC volatility (RS). Similarly, BNB and XRP share the most prominent information flow between volatilities estimated by GK, Parkinson's, and GK-YZ. The study presents the practicable addition of OHLC volatility estimators for quantifying the information flow and provides an additional choice to compare with other volatility estimators, such as stochastic volatility models.
Éder Johnson de Area Leão Pereira, Paulo Ferreira, Derick Quintino
Non-fungible tokens (NFTs) are a type of digital record of ownership used in a unique way: ensuring authenticity and uniqueness. Due to these characteristics, NFTs have been used in several markets: games, arts, and sports, among others. In 2020, the volume of negotiations of the NFTs was about USD 200 million. Despite the strong interest of economic agents in operating with NFTs, there are still gaps in the literature, regarding their dynamics and price interrelation with other potentially related assets, which deserve to be studied. In this sense, the main purpose in this paper is to analyze the cross-correlation between NFTs and larger cryptocurrencies. To this end, our methodological approach is based on a Detrended Cross-Correlation Analysis correlation coefficient, with a sliding windows approach. Our main finding is that the cross-correlations are not significant, except for a few cryptocurrencies, with weak significance at some moments of time. We also carried out an analysis of the long-term memory of NFTs, which demonstrated the antipersistence of these assets, with results seemingly corroborating the market inefficiency hypothesis. Our results are particularly important for different classes of investors, due to the analysis on different time scales.
Green cryptocurrencies have been recently created to reduce energy consumption and environmental pollution by adopting alternative mining practices. This paper examines for the first time the market of green cryptocurrencies for indication of herding behavior in the period of January 2017–June 2022. By using two measures that capture the proximity of asset returns from the market consensus, we conclude that herding behavior among investors in green cryptocurrencies was absent in the whole sample. However, the results of a subsample analysis and rolling window regression show that herding dynamics varied significantly throughout the sample period. The recent COVID-19 pandemic amplified the observed levels of herding behavior, suggesting that opportunities for diversification for investors operating in this market may have become more limited lately. For this reason, financial regulators should focus on the market of green cryptocurrencies if they want to promote the market’s efficiency necessary to attract additional investors.
This study aims to investigate the co-movement and Granger causality between Bitcoin prices (BTC) and M2 (cash, demand, and time deposits), inflation, and economic policy uncertainty (EPU) in the U.K. and Japan. It uses monthly data from 31 July 2010 to 30 August 2020 and employs the wavelet coherence method, Toda-Yamamoto, and nonlinear Granger-causality tests. The empirical results show that (i) Bitcoin prices influence M2 and interact with inflation and EPU. In the short term, inflation affects Bitcoin price positively, supporting Bitcoin as an inflation hedged instrument in Japan. Both in Japan and the U.K., the short-term effects of M2 on Bitcoin prices are negative, while EPU's effects on Bitcoin prices are positive, (ii) a bidirectional Toda-Yamamoto Granger causality exists between Bitcoin prices, inflation, and EPU and confirms that M2 affects Bitcoin prices, (iii) a nonlinear bidirectional causality exists between Bitcoin prices and inflation. While Bitcoin prices Granger cause M2 in the U.K. and Japan, inflation shows a nonlinear Granger causality with EPU in Japan. These findings help investors make investment decisions while considering the effects of M2, inflation, and EPU, and monetary authorities and policymakers make policies involving Bitcoin.
Bitcoin has gained popularity as an investment asset because of its similarity to gold, which sparked the idea that bitcoin can be used as a hedging instrument to the fiat currency exchange rate. This paper aims to analyze bitcoin's volatility and return to gauge its feasibility as an investment asset, and hedging tool for the USD-IDR exchange rate with the GARCH and EGARCH models. With data on the daily closing price of bitcoin, gold, IDX composite index, and USD-IDR exchange rate from January 1, 2016, to December 31, 2020, the study attempts to find factors affecting bitcoin returns with the independent variables of bitcoin’s price, gold, and USD-IDR exchange rate by estimating their correlation. Following the analysis, this study shows that the volatility of USD-IDR exchange rates negatively influences bitcoin returns, making it a relatively safe investment asset. Additionally, the study found that bitcoin returns are not affected by the variables of gold price and the IDX composite index. However, we found that the USD-IDR exchange rate significantly affects bitcoin returns, while gold price and bitcoin’s price does not significantly affect bitcoin returns. Further, the analysis found that bitcoin is unsuitable for hedging due to its sensitivity to asymmetric shocks.
Analyzing comovements and connectedness is critical for providing significant implications for crypto-portfolio risk management. However, most existing research focuses on the lower-order moment nexus (i.e. the return and volatility interactions). For the first time, this study investigates the higher-order moment comovements and risk connectedness among cryptocurrencies before and during the COVID-19 pandemic in both the time and frequency domains. We combine the realized moment measures and wavelet coherence, and the newly proposed time-varying parameter vector autoregression-based frequency connectedness approach (Chatziantoniou et al. in Integration and risk transmission in the market for crude oil a time-varying parameter frequency connectedness approach. Technical report, University of Pretoria, Department of Economics, 2021) using intraday high-frequency data. The empirical results demonstrate that the comovement of realized volatility between BTC and other cryptocurrencies is stronger than that of the realized skewness, realized kurtosis, and signed jump variation. The comovements among cryptocurrencies are both time-dependent and frequency-dependent. Besides the volatility spillovers, the risk spillovers of high-order moments and jumps are also significant, although their magnitudes vary with moments, making them moment-dependent as well and are lower than volatility connectedness. Frequency connectedness demonstrates that the risk connectedness is mainly transmitted in the short term (1-7 days). Furthermore, the total dynamic connectedness of all realized moments is time-varying and has been significantly affected by the outbreak of the COVID-19 pandemic. Several practical implications are drawn for crypto investors, portfolio managers, regulators, and policymakers in optimizing their investment and risk management tactics.
This research investigates the effects of several measures of Twitter-based sentiment on cryptocurrencies during the COVID-19 pandemic. Innovative economic, as well as market uncertainty measures based on Tweets, along the lines of Baker et al. (2021), are employed in an attempt to measure how investor sentiment influences the returns and volatility of major cryptocurrencies, developing on non-linear Granger causality tests. Evidence suggests that Twitter-derived sentiment mainly influences Litecoin, Ethereum, Cardano and Ethereum Classic when considering mean estimates. Moreover, uncertainty measures non-linearly influence each cryptocurrency examined, at all quantiles except for Cardano at lower quantiles, and both Ripple and Stellar at both lower and higher quantiles. Cryptocurrencies with lower values are found to be unaffected by investor sentiment at extreme values, however, prove to be profitable due to more aligned investor behaviour.
Erik Mauricio Muñoz Henríquez, Francisco A. Gálvez-Gamboa
<p>El objetivo de este trabajo es analizar el efecto spillover entre el mercado de las criptomonedas, los mercados financieros y commodities, utilizando índices de volatilidad realizada de las diez criptomonedas con mayor capitalización de mercado y la volatilidad implícita de las cotizaciones del Oro (GVZ) y el Petróleo (OVX), y el mercado financiero norteamericano (VIX) y europeo (VSTOXX) a través del Spillover Index basado en un Vector Autorregresivo (VAR). Los resultados indican que Ethereum es el mayor transmisor de volatilidad, seguido por Cardamo, mientras que los mayores receptores de volatilidad son ChainLink y BinanceCoin. Además, demostramos a través de la utilización de la volatilidad implícita que la contribución de los mercados financieros al spillover no excede el 3%, incluso resultados menores se evidencian con ambos commodities. El análisis de impulso-respuesta muestra el mayor efecto sobre las criptomonedas proviene del VIX, junto con una respuesta negativa ante un shock en el OVX y GVZ.</p>
Meiryani Meiryani, Caineth Delvin Tandyopranoto, Jason Emanuel, A. S. L. Lindawati · 7 authors
This study aims to determine the effect of global price movements for energy sector commodities, especially Crude Oil and Natural Gas Prices, on cryptocurrency price movements. This study focuses more on the Bitcoin cryptocurrency. This study uses quantitative methods, and the data collection used is secondary data with weekly data and the period from January 1, 2020-July 31, 2021. The number of observations used in this study amounted to 79 observations. Secondary data sources are obtained through the website finance.yahoo.com. The data processing technique will be carried out using Stata and SPSS software, the Multiple Linear Regression method, and the Classical Assumption Test. The results of this study show that global prices for energy sector commodities, especially Crude Oil, Natural Gas, have a positive effect on Bitcoin price movements. These results indicate a link between energy and Bitcoin caused by Bitcoin miners who are mining Bitcoin using energy so that when the price of Bitcoin rises, the price of energy will also increase.
Benjamin A. Jones, Andrew L. Goodkind, Robert P. Berrens
Abstract This paper provides economic estimates of the energy-related climate damages of mining Bitcoin (BTC), the dominant proof-of-work cryptocurrency. We provide three sustainability criteria for signaling when the climate damages may be unsustainable. BTC mining fails all three. We find that for 2016–2021: (i) per coin climate damages from BTC were increasing, rather than decreasing with industry maturation; (ii) during certain time periods, BTC climate damages exceed the price of each coin created; (iii) on average, each $1 in BTC market value created was responsible for $0.35 in global climate damages, which as a share of market value is in the range between beef production and crude oil burned as gasoline, and an order-of-magnitude higher than wind and solar power. Taken together, these results represent a set of sustainability red flags. While proponents have offered BTC as representing “digital gold,” from a climate damages perspective it operates more like “digital crude”.
Are conventional and sustainable cryptocurrencies effective hedging instruments for high cryptocurrency uncertainty? This paper examines co-movements between conventional (Bitcoin, Ethereum, Binance Coin, Tether) and sustainable (Cardano, Powerledger, Stellar, Ripple) cryptocurrencies and two cryptocurrency uncertainty indices (UCRY price and UCRY policy). Using weekly returns from 1 October 2017 to 30 March 2021, the paper employs the bivariate wavelet coherence method considering three investment horizons, short-term, medium-term, and long-term. The results confirm that conventional and sustainable cryptocurrencies show consistent positive and identical co-movements with both cryptocurrency uncertainty indices at the short-term horizon during COVID-19 and negative co-movement at the medium-term investment horizon, suggesting the short-term hedging ability of dirty/green cryptocurrencies for high UCRY price and policy. Evidence of negative coherences shows that higher cryptocurrency prices and policy uncertainties lead to lower cryptocurrency returns, reflecting the adverse impact of higher uncertainties on the trust of crypto traders and investors. Weak co-movement is found between dirty/green cryptocurrencies and UCRY price/policy indices, which suggests the possible role of dirty/green cryptocurrencies as a weak hedge for UCRY price and policy indices. These findings provide potential avenues to hedge cryptocurrency uncertainties using conventional and sustainable cryptocurrencies across multiple investment horizons.
Virtual digital assets including cryptocurrencies, non-fungible tokens and decentralized financial asset have been initially used as an alternative currency but are currently being purchased as an asset and hedging instruments. Exponentially growing trading volume witnesses the growing inclination of investors towards these assets, and this calls for volatility analysis of these assets. In this reference, the present study assessed and compared the volatility of returns from investment in virtual digital assets, equity and commodity market. Daily closing prices of selected cryptocurrencies, non-fungible tokens and decentralized financial assets, stock indices and commodities have been analysed for the post-covid period. Since returns were observed to be heteroscedastic, autoregressive conditional heteroscedastic models have been used to assess the volatility. The results indicate a low correlation of commodity investment with all other investment opportunities. Also, Tether and Dai have been observed to be negatively correlated with stock market. This indicates the possibility of minimizing risk through portfolio diversification. In terms of average returns, virtual digital assets are discerned to be better options than equity stock or commodity yet the variance scenario of these investment avenues is not very rosy. The volatility parameters reveal that unlike commodity market, virtual digital assets have got a significant impact of external shocks in the short-run. Further, the long run persistency of shocks is observed to be higher for the UK stock market, followed by Ethereum, Tether and Dai. The present analysis is crucial as the decision about its acceptance as legal tender money is still sub-judice in some countries. The results are expected to provide insight to regulatory bodies about these assets.
The present study examines the nonlinear relationship between the bitcoin prices and total bitcoin energy consumption over the period November 2010 and October 2021. A discrete threshold regression (TR) model is deployed to estimate the unknown thresholds that trigger the Bitcoin prices regime change. The designated TR model identifies six regimes of change for Bitcoin price movements. The estimated critical threshold specifications (total bitcoin energy consumption) that trigger the regime change of Bitcoin prices are estimated as 0.13, 2.52, 14.06, 43.17, and 146.29 respectively. The study finds that the impact of total bitcoin energy consumption on bitcoin prices are only statistically significant in the higher (4th and 6th) regimes respectively. The message here is that the impact of total bitcoin energy consumption on bitcoin prices is not uniform.
José Luís Miralles Quirós, María del Mar Miralles Quirós
Research background: A current strand of the financial literature is focusing on detecting inefficiencies, such as the day-of-the-week effect, in the cryptocurrency market. However, these studies are not considering that there are no daily closes in this market, and it is possible to trade cryptocurrencies on a continuous basis. This fact may have led to biases in previous empirical results. Purpose of the article: We propose to analyse the day-of-the-week effect on the Bitcoin from an alternative perspective where each hourly data in a day is considered an event. Focusing on that objective, we employ hourly closing prices for Bitcoin which are taken from the Kraken exchange, one of the world leading exchanges and trading platforms in the cryptocurrency markets, for the period spanning from January 2016 to December 2021. Methods: Contrary to the previous empirical evidence, we do not calculate daily returns, but rather the first stage of our proposed approach is devoted to analysing the hourly mean returns for each of the 24 hours of the day for each day of the week. We look for statistically significant hourly mean returns that could advance the importance of the hourly differentiation in the Bitcoin market. In a second stage, we calculate different post-event cumulative returns which are defined as the change in log prices over a time interval. Finally, we propose different investment strategies simply based on the significant hourly mean returns we obtain and we evaluate their performance in terms of the Sharpe ratio. Findings & value added: We contribute to the debate about the degree of Bitcoin?s market efficiency by providing an alternative methodology based on an event study hourly approach. Furthermore, we provide evidence that by investing in different post-event hourly windows it is possible to outperform the classic buy-and-hold strategy.
Portfolio risk management plays an important role in successful investments. Portfolio standard deviation, value-at-risk, expected shortfall, and maximum absolute deviation are widely used portfolio risk measures. However, the existing portfolio risk measures are vulnerable to larger skewness and kurtosis of the asset returns. Moreover, the traditional assumption of normality of the portfolio returns leads to the underestimation of portfolio risk. Cryptocurrencies are a decentralized digital medium of exchange. In contrast to physical money, cryptocurrency payments exist purely as digital entries on an online ledger called blockchain that describe specific transactions. Due to the high volume and high frequency of cryptocurrency transactions, risk forecasting using daily data is not enough, and a high-frequency analysis is required. High-frequency data reveal a very high excess kurtosis and skewness for returns of cryptocurrencies. In order to incorporate larger skewness and kurtosis of the cryptocurrencies, a data-driven portfolio risk measure is minimized to obtain the optimal portfolio weights. A recently proposed data-driven volatility forecasting approach with daily data are used to study risk forecasting for cryptocurrencies with high-frequency (hourly) big data. The paper emphasizes the superiority of portfolio selection of cryptocurrencies by minimizing the recently proposed risk measure over the traditional minimum variance portfolio.