Bitcoin price was exceed 60 thousand USD per one Bitcoin in March 2021. This fact manifested a general trend of rising cryptocurrency values during COVID-19 pandemic. Hence the related question, is it worth investing in cryptocurrency? The answer to this question depends on many factors, one of the decisive ones is the high volatility of cryptocurrency. Current work considers volatility and profitability of cryptocurrency, and discusses how to determine volatility and profitability of cryptocurrencies and its importance in investing. In addition, the profitability/losses of cryptocurrency transactions are analysed. If cryptocurrency will be stable in the future, then it is easily accepted through worldwide and in the long run, people would have more trust to the cryptocurrency and its usability.
Purpose Motivated by the lure of cryptocurrencies for retail investors, whose concentrated holdings are particularly exposed to price crash risk, this paper aims to study the relationship between investor attention and crash risk for a range of cryptocurrencies. Design/methodology/approach This study adopts a quantile regression approach to determine the effect of investor attention on crash risk. Crash risk is measured using the negative coefficient of skewness and down up volatility. Findings This study finds that the connection is concentrated in the tails of the crash risk distribution. Investor attention has a positive relationship with crash risk when crash risk is low (below-median quantiles) and negative when crash risk is high (above-median). The findings are consistent for different measures of crash risk, for alternate internet searches and for a panel of large cryptocurrencies in addition to Bitcoin. This study also notes seasonality in crash risk, with higher crash risk during the June–August period and lower crash risk in the Halloween period that runs from November to April. Originality/value The results provide insights that are not apparent in previous analyses of cryptocurrency price crash risk. The results are particularly important for retail investors, who constitute a large portion of the cryptocurrency market, as they tend to hold concentrated investments and so a price crash of a single asset may have a large bearing on their wealth.
Cryptocurrencies have become popular. Economic agents use cryptocurrency such as bitcoins to make payments and it pose a threat to fiat currency. Central banks have begun to respond to this threat. They realize that they need to join the race to offer a digital currency and dominate the digital currency landscape which can lead to the collapse of most private digital currencies that are not issued by a central bank or a monetary authority. In this paper, I show how the issuance of a central bank digital currency can lead to the collapse of private digital currencies such as bitcoin. I argue that central banks will leverage on their monetary powers, and the trust that citizens have in government-backed money. This may give central banks strong incentives to issue a central bank digital currency. The issuance of a central bank digital currency can erode trust in cryptocurrencies, and lead to lack of trust in cryptocurrency, thereby leading to the collapse of cryptocurrencies although not immediately.
Can technology protect investors from extreme losses? This paper investigates the short- and long-run hedging and safe haven properties of Bitcoin for the US dollar over the period 2010-2023, incorporating the COVID-19-related market turmoil. Our findings reveal that (i) Bitcoin acts as a strong hedge for all US dollar currency pairs examined, (ii) Bitcoin functions as a weak safe haven for the US dollar at short investment horizons, as indicated by a limited relationship during acute negative price movements, (iii) Bitcoin, instead of acting as a safe haven may, instead, increase aggregate risk at long horizons during periods of extreme losses. The analysis, performed using a series of horizon-dependent econometric tests, provides evidence of some US dollar risk-reduction benefits from Bitcoin but limited potential for enduring relief from long-run extreme negative US dollar rate movements.
This paper examines the spillover effect between bitcoin, gold, crude oil, and major stock markets by using the MSV model with dynamic correlation and Granger causality. The empirical results of the DC‐GC‐MSV model are logically correct and convergent. The DIC test result has proved that the DC‐GC‐MSV model is better and more accurate. Bitcoin has no significant Granger causality spillover effect than other assets. As a safe haven product for stock assets, gold price has one‐way spillover effect from stock market volatility. Moreover, crude oil has the highest correlation with the stock market. In the recent COVID‐19 epidemic and the sluggish economic environment, investors need to consider a balanced asset allocation among low‐correlation assets, medium‐correlation assets, and high‐correlation assets to reduce risks.
We rationalize cryptocurrency adoption in a small open economy model. We show that digital dollarization, where stablecoins pegged to the USD are used for transactions, can improve social welfare. In contrast, the adoption of volatile cryptocurrencies, such as El Salvador’s 2021 decision to make Bitcoin legal tender, results in welfare losses. This outcome aligns with the observed low take-up of Bitcoin as legal tender. The welfare benefits of digital dollarization increase with the magnitude of macroeconomic shocks, providing motivation for the growing use of stablecoins in emerging markets as a safeguard against high inflation and macroeconomic instability .
This study investigates how uncertainty surrounding cryptocurrency affects cryptocurrency return (CR) by employing various wavelet techniques. To this end, we concentrate on the recently published cryptocurrency uncertainty index (UCRY) and the top eight cryptocurrencies by virtue of market capitalization for the period from December 30, 2013, until February 21, 2021. Our results show that the UCRY index strongly predicts CR. In particular, the UCRY index has a leading position in all the frequencies for all cryptocurrencies in our sample. Additionally, when the impacts of economic policy uncertainty and the volatility index are eliminated, the significant co-movement of UCRY-CR stays unchanged for short-, medium-, and long-term investment horizons. Thus, we conclude that the UCRY-CR relationships are both persistent and pervasive. Our study contributes to the literature on the relationships between cryptocurrency and market uncertainties as well as to investors who use uncertainty indices to design their investment strategies for their portfolios.
Francis Liu, Natalie Packham, Meng-Jou Lu, Wolfgang Karl Härdle
The introduction of derivatives on Bitcoin enables investors to hedge risk exposures in cryptocurrencies. Because of volatility swings and jumps in cryptocurrency prices, the traditional variance-based approach to obtain hedge ratios may not be suitable for hedgers. In this work, we consider two extensions of the traditional approach: first, different dependence structures are modelled by different copulae, such as the Gaussian, Student-t, Normal Inverse Gaussian and Archimedean copulae; second, different risk measures, such as value-at-risk, expected shortfall and spectral risk measures are employed to find the optimal hedge ratio. Extensive out-of-sample tests using the data from the time period December 2017 until May 2021 give insights in the practice of hedging various cryptos and crypto indices, including Bitcoin, Ethereum, Cardano, the CRIX index and a number of crypto-portfolios. Evidence shows that BTC futures can effectively hedge BTC and BTC-involved indices. This promising result is consistent across different risk measures and copulae except for the Frank copula. On the other hand, we observe complex and diverse dependence structures between non-BTC-related cryptocurrencies and the BTC futures. As a consequence, the hedge performance of non-BTC-related cryptocurrencies is mixed and even suitable for some assets.
<abstract> <p>The aim of this study was to examine the returns and volatility of Bitcoin. The study uses the daily closing price of Bitcoin from October 1, 2013 to July 31, 2020 as the sample data, which include 2496 observations. About the methodology, the paper describes the utilisation of GARCH models to analyse Bitcoin's returns and volatility. First, the data were tested by using the augmented Dickey-Fuller test to verify the stability and diagram tests sequence. After that, the lag order and determination results of the mean value equation show that the Lag 4 period is the best. Additionally, the paper describes an autocorrelation test of the residual series, which revealed that there is no significant autocorrelation in the residual term for the Bitcoin returns, but that the residual squared has significant autocorrelation. In addition, a linear graph of squared residuals was formulated and the ARCH-LM test was used to find the data that are suitable for modelling with GARCH models since the data have a strong ARCH effect. As result, a GARCH (1, 1) model was used; the findings indicated that the returns and volatility of Bitcoin have clustering characteristics, and that the returns and volatility of Bitcoin constitute a persistent process although the effects gradually reduce over time. Because of the limitations of the GARCH (1, 1) model and researching asymmetry of the returns and volatility of Bitcoin, TARCH and EGARCH models were adopted; the findings indicated that the returns and volatility of Bitcoin are without a "leverage effect". To further explain this special phenomenon, safe-property is quoted in this research. In the end, this paper demonstrates that Bitcoin, as a safe-haven property, can hedge financial risks in times of economic depression. Besides, Bitcoin has a revised asymmetric effect between positive and negative shocks that makes it a viable asset to add to the portfolios of investors.</p> </abstract>
This article considers a variety of highly diversified cross-sectional momentum and reversal strategies, with sorting and holding periods from one week up to two years. In a sample of the 2,000 largest cryptocurrencies during the period 2014–2020, we identify positive momentum on short horizons up to two to four weeks and a significant reversal on longer horizons beyond one month. The reversal effect becomes more pronounced once we expand the sorting and/or holding periods. Momentum and, particularly, reversal returns are economically large, statistically significant, and generally not exposed to standard cryptocurrency risk factors. The main drivers of the reversal effect are “past loser” cryptocurrencies. The switching of momentum into reversal occurs after approximately one month—much quicker than the equity market, and evidence of the “faster metabolism of cryptocurrencies.”
Abstract About 99% of cryptocurrency trades occur on organized exchanges with many investors subsequently keeping their digital assets in accounts with cryptocurrency markets. This generates exposure to the risk of exchange closures. We construct a database containing eight key characteristics on 238 cryptocurrency exchanges and employ machine learning techniques to predict whether a cryptocurrency market will remain active or whether it will go out of business. Both in‐sample and out‐of‐sample measures of forecasting performance are computed and ranked for four popular machine learning algorithms. Although all four models produce satisfactory classification accuracy, our best model is a random forest classifier. It reaches accuracy of 90.4% on training data and 86.1% on a test dataset. From the list of predictors, we find that exchange lifetime, transacted volume, and cyber‐security measures such as security audit, cold storage, and bug bounty programs rank high in terms of feature importance across multiple algorithms. On the other hand, whether an exchange has previously experienced a security breach does not rank highly according to its contribution to classification accuracy.
Nishant Jagannath, Tudor Barbulescu, Karam M. Sallam, Ibrahim Elgendi · 8 authors
Bitcoin generates a massive amount of data every day due to its innate transparency and capacity of operating completely decentralised. In this paper, we introduce on-chain metrics derived from data on the bitcoin network that enable us to describe the state and usage of the underlying network. Based on their characteristics, we classify them into user, miner, exchange activities and run a correlation analysis with the price to understand the dynamics of bitcoin's price and its underlying mechanics. Using the correlated data, we develop a deep learning model. However, determining the best values of parameters in a deep learning model can be a very challenging and time-consuming task. Hence, we propose a self-adaptive technique using a jSO optimization algorithm to find the best values of these parameters to accurately predict the price of bitcoin. Compared to traditional LSTM model, our approach is highly accurate and optimised with a minimum error rate.
The economic risk of the carbon footprint of the Bitcoin network remains unexplored. We develop the real-time artificial price for the carbon footprint of the Bitcoin network and thereby extend the climate value at risk (VaR) into the climate expected shortfall (ES) by employing both parametric and semiparametric models. On the basis of the best-fitted climate VaR and ES estimations, we find that the 95th percentiles (upper bound) of the climate VaR and ES are 8.04 and 10.37 billion euros, respectively, and the 99th percentiles (upper bound) of climate VaR and ES are 11.33 and 14.15 billion euros, respectively. Moreover, given the climate VaR and ES estimations on the basis of similar carbon footprint, the negative environmental externality of the Bitcoin network based on the current carbon price is not sufficient to reflect the environmental cost. Overall, our research provides new insight into the linkage between the Bitcoin network and the environment, which will provide meaningful information for both investors and policymakers.
In this study, the Log-Periodic Power Law Singularity (LPPLS) model is adopted for real-time identification and monitoring of Bitcoin bubbles and crashes using different time scale data, and the modified Lagrange regularization method is proposed to alleviate the impact of potential LPPLS model over-fitting to better estimate bubble start time and market regime change. The goal here is to determine the nature of the bubbles and crashes (i.e., whether they are endogenous due to their own price evolution or exogenous due to external market and/or policy influences). A systematic market event analysis is performed and correlated to the Bitcoin bubbles detected. Based on the daily LPPLS confidence indictor from 1 December 2019 to 24 June 2021, this analysis has disclosed that the Bitcoin boom from November 2020 to mid-January 2021 is an endogenous bubble, stemming from the self-reinforcement of cooperative herding and imitative behaviors of market players, while the price spike from mid-January 2021 to mid-April 2021 is likely an exogenous bubble driven by extrinsic events including a series of large-scale acquisitions and adoptions by well-known institutions such as Visa and Tesla. Finally, the utilities of multi-resolution LPPLS analysis in revealing both short-term changes and long-term states have also been demonstrated in this study.
Alexander Guzmán, Cristian Pinto‐Gutiérrez, María Andrea Trujillo Dávila
This paper examines the impact of COVID-19 lockdowns on Bitcoin trading volume. Using data from Apple mobility trends and several time-series econometric models, we find that investors became active participants during the COVID-19 pandemic period and traded more bitcoins on days with low mobility associated with lockdown mandates. These results remain robust after controlling for stocks and gold returns, the VIX index, and the level of attention and sentiment toward Bitcoin, as measured by Google search frequencies and the tone of Tweets discussing Bitcoin. These results suggest that when individual investors have ample free time on their hands, they trade cryptocurrencies as a pastime and use the Bitcoin market as a form of entertainment. Moreover, our results have important implications concerning investors’ herding behavior and overconfidence leading to noise trader risks and bubbles typically accompanied by high trading volume in cryptocurrency markets.
Abstract We examine the association of Bitcoin, and other cryptocurrency, returns with changes in inflation expectations, and form a comparison with gold, a traditional inflation hedge. We control for uncertainty in economic policy, cryptocurrency, and financial markets, and show that cryptocurrency returns are positively related to changes in US inflation expectations only for a limited set of circumstances. Unlike with gold, the identified relationship is only significant for short‐term inflation expectations, and when inflation or market‐implied inflation expectations are below 2% (the Fed's inflation target). Moreover, cryptocurrency returns tend to be lower on days with monthly consumer price index (CPI) announcements and respond negatively to CPI surprises. Our results suggest that cryptocurrencies do not currently offer investors a viable alternative to gold for hedging inflation.