We investigate how key features associated with the Proof-of-Work consensus mechanism of Bitcoin (commonly referred to as mining) affect pricing. In a controlled laboratory experiment, we observe that price bubble formation can be attributed to mining. Moreover, overpricing is more pronounced if the mining capacity is centralized to a small group of individuals. The order book data reveal that miners seem to play a crucial role in bubble formation. Further probing the mechanism in a second study, we find that both mining costs and decisions jointly with the sluggish rate of supply of the asset contribute to the bubble formation. Our results demonstrate that erratic pricing is an inherent feature of cryptocurrencies based on a mining protocol, thus seriously limiting any prospects for such assets becoming a medium of exchange. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: The funding provided by the University of Heidelberg, Hanken Foundation [Grant 271-6250], and Durham University is gratefully acknowledged. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01238 .
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.
In this article, we establish a method to detect and formulate price bubbles in the cryptocurrency markets. This method identifies abnormal crashes through violations of the exponential decaying property. Confirmations of bubble bursts within these anomalies are obtained through wavelet analysis. By decomposing the cryptocurrency price into the high-frequency and low-frequency factors, we distinguish the price regimes versus the periods with bubbles and crashes in both time and frequency domains. In addition, we apply the log-periodic power law model to fit the bubble formation. In the analysis of eight cryptocurrencies—Bitcoin, Ethereum, Litecoin, Antshares, Ethereum Classic, Dash, Monero, and OmiseGO—from 15 May 2018 to 28 November 2022, we identify 24 bubbles. Some of them exhibit a significant and strong exponential growth pattern.
Fırat Akba, İ̇hsan Tolga Medeni, Mehmet Serdar Güzel, I. N. Askerzade
Today, there are constant changes in terms of securities in stock markets. In these stock market investments, investors use fundamental analysis tools and indicators very widely. In this way, it is possible to have some knowledge of the situations experienced in the markets and to make a profit. In this study, manipulations on Bitcoin are discussed. Popular machine and statistical forecasting methods have been used to detect these manipulations and the road maps to be followed in order to be detected in the most successful way have been shared. Social media sentiments, which were thought to have an effect on manipulations during the studies, were also evaluated with the most advanced text analysis methods and evaluated together with these price changes. The allegations that the prediction methods carried out before the crisis were more successful were investigated. The Covid-19 pandemic was evaluated as a period of global crisis and the studies that might be relevant were examined. It would not be wrong to say that the actors that make big gains in the stock markets are the ones that determine the direction of the stock market. The manipulation periods of the market actors to be successful in the virtual money markets have been tried to be verified by various estimation methods. These estimations can achieve up to F1score of 93% success according to our experimental result. Besides, it is stated that accounts with the highest volume of transactions in the periods, when anomalies were detected, were labeled as potential manipulators.
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.
The emergence of Bitcoin poses an important question for monetary theorists: can Bitcoin compete with, or even replace existing fiat monies? To answer this question, one must be able to determine what gives intrinsically useless monies their value, what determines the coexistence of alternative monies, and under what conditions economic agents would prefer to hold one money relative to another. We attempt to answer these questions in light of the emergence of Bitcoin. In particular, we outline a theoretical model in which an intrinsically useless money is essential.
Jakob Albers, Mihai Cucuringu, Sam Howison, Alexander Y. Shestopaloff
In light of micro-scale inefficiencies induced by the high degree of fragmentation of the Bitcoin trading landscape, we utilize a granular data set comprised of orderbook and trades data from the most liquid Bitcoin markets, in order to understand the price formation process at sub-1 second time scales. To achieve this goal, we construct a set of features that encapsulate relevant microstructural information over short lookback windows. These features are subsequently leveraged first to generate a leader-lagger network that quantifies how markets impact one another, and then to train linear models capable of explaining between 10% and 37% of total variation in $500$ms future returns (depending on which market is the prediction target). The results are then compared with those of various PnL calculations that take trading realities, such as transaction costs, into account. The PnL calculations are based on natural $\textit{taker}$ strategies (meaning they employ market orders) that we associate to each model. Our findings emphasize the role of a market's fee regime in determining its propensity to being a leader or a lagger, as well as the profitability of our taker strategy. Taking our analysis further, we also derive a natural $\textit{maker}$ strategy (i.e., one that uses only passive limit orders), which, due to the difficulties associated with backtesting maker strategies, we test in a real-world live trading experiment, in which we turned over 1.5 million USD in notional volume. Lending additional confidence to our models, and by extension to the features they are based on, the results indicate a significant improvement over a naive benchmark strategy, which we also deploy in a live trading environment with real capital, for the sake of comparison.
Andrii Bielinskyi, Oleksandr Serdyuk, Сергій Олексійович Семеріков, Vladimir Soloviev
Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.
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 study interrelates three adjacent topics in data evaluation. The first is the establishment of a relationship between Bregman divergence and probabilistic alpha-divergence. In particular, we demonstrate that square-root-order probability normalization enables the unification of these two divergence families. This yields a new alpha-divergence, which can be used to jointly derive the alpha-EM algorithm (alpha-expectation-maximization algorithm) and the traditional log-EM algorithm. The second topic is the application of the alpha-EM algorithm in the evaluation of graders scoring raw data over a network. We estimate multinomial mixture distributions in this evaluation problem. We note that the convergence speed of the alpha-EM algorithm is significantly higher than that of the log-EM algorithm. Finally, the third topic is the use of this increase in convergence speed to assign the winning evaluator and miner in a blockchain environment. This is achieved by proof-of-review using evaluation scores, which is a class of proof-of-stake. In the second and third topics, we select terminology from wine tasting for brevity in the exposition. However, this formulation can be applied to a broader class of data in a network environment comprising blockchains.
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.”
This paper uses a search-theoretic model to study conditions under which cryptocurrency is valued and under which it coexists with fiat money. In my model, a cryptocurrency economy is one in which private agents’ decisions determine the stock of money and in which the marginal cost of producing money is increasing in the existing nominal stock. I show that the inflation rate of cryptocurrency must be zero in a stationary monetary equilibrium. This result is in sharp contrast to models with fiat money in which the stock of money is exogenously given. In fiat money economies, the inflation rate is determined by the rate of growth of the money stock. My result is also in sharp contrast with other types of private money economies, in which the inflation rate must necessarily be different from zero. In such private money economies, the cost of producing additional money does not depend on the existing nominal stock. Moreover, I show that cryptocurrency and fiat money can circulate at the same time and that the rates of return on these two assets may not be the same. Competition with cryptocurrency restricts the government’s ability to over-issue fiat money and thereby might improve on pure fiat money equilibria without government commitment.
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.
Abstract We analyze cryptoasset markets (cryptocurrencies and stablecoins) at high frequency. We investigate intraday patterns. We show that Tether plays a crucial role as a safe haven and/or store of value facilitating trading in cryptocurrencies without going through traditional currencies. Markets centered on cryptocurrencies and stablecoins play a primary role aggregating preference/technology shocks and heterogeneous opinions, instead markets centered on the US dollar play a marginal role on price formation.