Blockchain Papers

James Yae, George Zhe Tian

No abstract is available for this record.

Lai T. Hoang, Dirk G. Baur

No abstract is available for this record.

Hubert Anciaux, Christophe Desagre, Nicolas Nicaise, Mikaël Petitjean

No abstract is available for this record.

Cyprian Ondieki Omari, Anthony Ngunyi

This paper implements the analysis of volatility behaviour of the eight major cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, Monero, Stellar, Dash and Tether) for the period starting from October 13th 2015 to November 18th 2019. The GARCH-type models with heavy-tailed distributions are fitted to filter the conditional volatility exhibited by cryptocurrencies. Extreme value analysis based on the peak over threshold approach is then used to model the extreme tail behaviour of the cryptocurrencies. The predictive performance of the GARCH-EVT model in forecasting Value-at-Risk is evaluated at both 5% and 1% levels of significance. The backtesting results demonstrate the superiority of the GARCH-EVT model in both out-of-sample forecasts and goodness-of-fit properties to cryptocurrency returns and forecasting Value-at-Risk. Overall, the empirical results of this study recommend the heavy-tailed GARCH-EVT based model for modelling and forecasting the volatility of cryptocurrencies.

Cem Çağrı Dönmez, Doruk Şen, Ahmet Fatih Dereli, Muhammed Bilal Horasan · 6 authors

Recent developments in global financial markets revealed that cryptocurrencies experienced rapid growth due to the popularity of blockchain technology and its evolving position in the digital finance industry. The rise of cryptocurrencies led economists to question generally accepted financial practices. Particularly the interaction between two different types of financial markets arose as a hot research topic to discover specific relationships and differences between major cryptocurrencies and fiat currencies. Therefore, this article aims to examine analyze by attaching importance to the Bitcoin to investigate significant linkages and analyze critical direct and indirect connections. In this research, Bitcoin—which is known as the most prominent cryptocurrency on the market—and 50 different conventional currencies are taken into consideration by applying cross-correlation, HT (hierarchical tree), and MST (minimum spanning tree) methods. The results of this work can be utilized by academicians and economists for further research related to the subject.

Yizhou Cao, Min Dai, Steven Kou, Lewei Li · 5 authors

Abstract Existing cryptocurrencies are too volatile to be used as currencies for daily payments. Stablecoins, which are cryptocurrencies pegged to other stable financial assets such as the US dollar, are desirable for payments within blockchain networks, whereby being often called the “Holy Grail of cryptocurrency.” By using the option pricing theory and the Ethereum platform that allows running smart contracts, we design several dual‐class structures that are written on the ETH cryptocurrency and offer a fixed‐income crypto asset (Class A coin), a stablecoin (Class A′ coin) pegged to a traditional currency, and leveraged investment instruments (Class B and B′ coins). Our investigation of the values of stablecoins in the presence of jump risk and black swan‐type events shows the robustness of the design. The design has been implemented on the Ethereum platform.

Roman Matkovskyy, Akanksha Jalan

Dans cette étude, nous quantifions et analysons la dépendance dynamique entre les rendements du marché des bitcoins aux États-Unis, dans la zone euro, au Royaume-Uni et au Japon et l’inflation réalisée et inattendue, sous réserve de différents états du marché et de diverses nuances d’inflation. En utilisant une régression quantile sur quantile, nous étudions les propriétés de couverture du bitcoin contre l’inflation, offrant ainsi un nouveau regard sur le puzzle du retour de l’inflation du point de vue des investissements alternatifs. Nous constatons que tandis que les marchés haussiers du Royaume-Uni, de l’euro et du bitcoin japonais facilitent la couverture contre l’inflation en offrant des rendements plus élevés, le marché du bitcoin USD se comporte moins bien avec l’inflation. En général, nos résultats indiquent une relation asymétrique entre l’inflation, à la fois réalisée et inattendue, et les investissements alternatifs tels que le marché du bitcoin.

Konstantin Häusler, Hongyu Xia

Abstract Several cryptocurrency (CC) indices track the dynamics of the rising CC sector, and soon ETFs will be issued on them. We conduct a qualitative and quantitative evaluation of the currently existing CC indices. As the CC sector is not yet consolidated, index issuers face the challenge of tracking the dynamics of a fast-growing sector that is under continuous transformation. We propose several criteria and various measures to compare the indices under review. Major differences between the indices lie in their weighting schemes, their coverage of CCs and the number of constituents, the level of transparency, and thus, their accuracy in mapping the dynamics of the CC sector. Our analysis reveals that simple market cap-weighted indices outperform their competitors. Interestingly, increasing the number of constituents does not automatically lead to a better fit of the CC sector. All codes are available on "Image missing".

Daniele Bianchi, Mykola Babiak

We investigate the dynamics of daily realised returns and risk premiums for a large cross-section of cryptocurrency pairs through the lens of an Instrumented Principal Component Analysis (IPCA) (see Kelly et al., 2019). We show that a model with three latent factors and time-varying factor loadings significantly outperforms a benchmark model with observable risk factors: the total (predictive) R2 from the IPCA is 17.2% (2.9%) for individual returns, against a benchmark 9.6% (-0.02%) obtained from a model with six observable risk factors explored in previous literature. By looking at the characteristics that significantly matter for the dynamics of risk premiums, we provide robust evidence that liquidity, size, reversal, and both market and downside risks represent the main driving factors behind expected returns. These results hold for both individual assets and characteristic-based portfolios, pre and post the Covid-19 outbreak, and for weekly individual and portfolio returns.

Marco Lambrecht, Andis Sofianos, Yilong Xu

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 .

Savva Shanaev, Binam Ghimire

No abstract is available for this record.

Татьяна Валентиновна Антипова

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.

Yosef Bonaparte

No abstract is available for this record.

Junhuan Zhang, Haodong Wang, Jing Chen, Anqi Liu

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.

Daniele Bernardi, Ruggero Bertelli

No abstract is available for this record.

Winston Wei Dou, Xiang Fang, Andrew W. Lo, Harald Uhlig

No abstract is available for this record.

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.

Andria van der Merwe

No abstract is available for this record.

Peterson K Ozili

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.

Joshua R. Hendrickson, William J. Luther

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.

Li Guo, Bo Sang, Jun Tu, Yu Wang

No abstract is available for this record.

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.

Thomas Conlon, Shaen Corbet, Richard McGee

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.