Blockchain Papers

Aktham Maghyereh, Hussein Abdoh

No abstract is available for this record.

Shaad Iqbal Ansari, H Y Vani

In this article forecasting of daily closing price series of Bitcoin, Ripple, Dash, Litecoin and Ethereum crypto currencies, using data on prices (open, low, high), market capital and volumes using prior days is focused. The value conduct of cryptographic forms of money remains to a great extent neglected, giving new chances to scientists and business analysts to feature the likenesses and contrasts with standard monetary costs. Hence the paper is focused on this area. he results are compared with various benchmarks. Predictions are done using statistical techniques and machine learning algorithms. A simple linear regression (SLR) model that uses only a single-variable sequence of closing prices for forecasting, and a multiple linear regression (MLR) model that uses a multivariate sequence of prices and quantities at the same time. The simple linear regression (SLR) model for univariate serial forecasting uses only closing prices. Mean Absolute Percentage Error (MAPE) and relative Root Mean Square Error (relative RMSE) performance measures are considered. The accuracy achieved by the ARIMA model on our dataset is the highest, followed by Multivariable Linear Regression and LSTM.

Jithin V. Nair, Parthajit Kayal

This study is an extension of the work by Xu et al. (2021 , Finance Research Letters, vol. 38, p. 101453). We analyse tail-risk spillovers among 63 cryptocurrencies and identify systemically important cryptocurrencies using a Tail-Event driven NETwork (TENET)-based approach as proposed by Fan et al. (2018 , Journal of Business & Economic Statistics, vol. 36, pp. 212–226). We observe that cryptocurrencies with high market capitalization, such as Bitcoin, Ethereum and XRP, have weaker spillovers compared to other cryptocurrencies. We find that Bitcoin is the largest systemic risk receiver and Bitcoin Cash is the largest systemic risk emitter. Bitcoin Cash is the most interactive cryptocurrency. This study helps in understanding tail-risk dependency across cryptocurrencies and thereby can be valuable for portfolio creation and risk mitigation purposes.

Anupam Dutta, Elie Bouri

We examine the presence of outliers and time-varying jumps in the returns of four major cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin, Litecoin), and a broad cryptocurrency index (CCI30). The results indicate that only Bitcoin returns are contaminated with outliers. Time-varying jumps are present in Bitcoin, Litecoin, Ripple, and the cryptocurrency index. Notably, the presence of jumps in Bitcoin is significant after correcting for outliers. The main findings point to a price instability in some major cryptocurrencies and thereby the importance of accounting for large shocks and time-varying jumps in modelling volatility in the debatable cryptocurrency markets.

Natividad Blasco, Pilar Corredor, Nerea Satrústegui

This paper analyses the herding behaviour among exchanges around the expiration of bitcoin futures traded on the Chicago Mercantile Exchange (CME). The database extends from December 2017 to October 2020, taking as a reference the main exchanges that trade bitcoin (Binance, Bitfinex, Bitstamp, Coinbase, itBit, Kraken, and Gemini) and using hourly closing prices and trading volumes in bitcoin and US dollars. Adapting the proposal of Chang, Cheng and Khorana (2000) (CCK) to test conditional herding, we obtain results that indicate that the herding effect is significant during the week before expiration. After expiration, the herding effect lasts for a few hours and disappears. Information overload originating, among other causes, from sophisticated investors' strategies may generate this mimetic behaviour. The results show the relevance of intraday data applied to specific events such as expiration since the unconditional analysis shows, in general, anti-herding behaviour throughout the period of study.

Bertrand Crettez, Lisa Morhaim

No abstract is available for this record.

Viviane de Senna, Adriano Mendonça Souza

No abstract is available for this record.

Valeria D’Amato, Susanna Levantesi, Gabriella Piscopo

No abstract is available for this record.

Gaurav Kumar

The cryptocurrencies are a hot topic in the global financial system. Cryptocurrency is a digital or virtual or internet currency that uses cryptography for security. Cryptocurrency has created unmatched changes in the financial market having both positive and negative contributions. The concept of cryptocurrency is a little hard to accept, but it is easy to use. It is considered difficult because it is entirely different from our conventional currencies that we people are using since ages. Here, we focus the different types of cryptocurrencies, origin and evolution of the term. The role of cryptography in early cryptocurrencies, Issues currently associated with the term, the role of cryptography in today’s cryptocurrencies, cryptocurrencies exchanges, Cryptocurrencies Trading, advantages and disadvantages of cryptocurrencies trading, How Many cryptocurrencies are there? Market Capitalization of Cryptocurrency, the 2021 Global Crypto Adoption Index Top 20, One Year change in the value of Crypto Assets.

I. S. Ivanchenko

Subject. This article explores the cryptocurrency market and the changes in the three most popular cryptocurrencies currently, namely Bitcoin, Ethereum and Tether, in particular. Objectives. The article aims to answer the question whether it is possible to predict the cryptocurrency rate taking into account the high market value volatility or not. Results. Testing the cryptocurrency market for information efficiency made it possible to choose the most adequate model for predicting the market prices of cryptocurrency, namely the Heterogeneous Autoregressive model of Realized Volatility – HAR-RV model. Despite the simplicity of the structure, the HAR-RV model shows good results in predicting the market prices of cryptocurrency. Taking into account that forecasting the changes in time series using regression models fails with unexpected spikes in market information, the Shannon entropy gets calculated, the values of which warn the researcher in advance about the growth or decline of the cryptocurrency rate. The article proposes to enhance the predictive properties of the HAR-RV model by calculating the Shannon information entropy for the studied time series. Conclusions and Relevance. Currently, despite the high volatility of the cryptocurrency, the changes in its market price can be predicted quite accurately. Cryptocurrency meets all the Austrian School's requirements for money, and in the future, it will be able to compete with fiat currencies significantly. The proposed method of forecasting the changes in time series can be used by analysts and traders concerning their stock, exchange, and money market activities.

Yongkil Ahn

No abstract is available for this record.

Bingbing Dong, Lei Jiang, Jinyu Liu, Yifeng Zhu

No abstract is available for this record.

Eugene Yu. Shchetinin

The paper proposes a trading strategy for investing in the cryptocurrency market that uses instant market entries based on additional sources of information in the form of a developed dataset. The task of predicting the moment of entering the market is formulated as the task of classifying the trend in the value of cryptocurrencies. To solve it, ensemble models and deep neural networks were used in the present paper, which made it possible to obtain a forecast with high accuracy. Computer analysis of various investment strategies has shown a significant advantage of the proposed investment model over traditional machine learning methods.

Rebekka Buse, Konstantin Görgen, Melanie Schienle

We study the prediction of Value at Risk (VaR) for cryptocurrencies. In contrast to classic assets, returns of cryptocurrencies are often highly volatile and characterized by large fluctuations around single events. Analyzing a comprehensive set of 105 major cryptocurrencies, we show that Generalized Random Forests (GRF) (Athey, Tibshirani & Wager, 2019) adapted to quantile prediction have superior performance over other established methods such as quantile regression, GARCH-type and CAViaR models. This advantage is especially pronounced in unstable times and for classes of highly-volatile cryptocurrencies. Furthermore, we identify important predictors during such times and show their influence on forecasting over time. Moreover, a comprehensive simulation study also indicates that the GRF methodology is at least on par with existing methods in VaR predictions for standard types of financial returns and clearly superior in the cryptocurrency setup.

David Bourghelle, Fredj Jawadi, Philippe Rozin

In this paper, we build an empirical specification that helps to explain bitcoin volatility and to characterize phases of the bitcoin bubble using information derived from investors’ emotions and sentiment that captures investment intentions and investors’ aversion to risk. To this end, we investigated the bilateral relations between bitcoin volatility and investor emotions between 2018 and 2021, a period characterized by significant changes in bitcoin prices as well as wide disparities in investor emotions, especially in the context of the ongoing COVID-19 pandemic. The study was based on a linear and nonlinear Vector Autoregressive (VAR) model that we applied to data related to bitcoin prices and market sentiment as expressed by the Fear and Greed index. Overall, our results evince the key role played by collective emotions in the formation and collapse of the bitcoin bubble. Two findings in particular stand out. First, our model shows significant time-varying lead-lag effects between bitcoin volatility and investor sentiment that come into play bilaterally and help to characterize the dynamics of bitcoin volatility. Second, these interactions exhibit asymmetry and nonlinearity as the sign and size of collective emotions (resp. bitcoin volatility) vary with the regime and market state under consideration (calm state versus period of bubble formation, etc.). In other words, the power of sentiment has a time-varying effect on the market. Indeed, in the first regime (“calm state”), where bitcoin volatility is relatively low and the market shows evidence of stability, collective emotions have a negative impact on bitcoin volatility, prompting a stabilizing strength. However, in the second regime (“bubble formation”), the effect of emotions turns significantly positive as investors gradually become less fearful and more reassured, which can simultaneously increase volatility and destabilize the market. Finally, in the third regime (“bubble collapse”), when bitcoin reaches a high level of value and experiences impressive volatility excess , the effect of emotions again turns negative, resulting in further switching behavior that pushes investor action to provoke a bitcoin price correction, moving it toward a new state of stability. Our conclusion helps improve predictions of bitcoin price dynamics informed by the information provided by investor emotions.

Amin Najafgholizadeh, Arman Nasirkhani, Hamidreza Mazandarani, Hamid Reza Soltanalizadeh · 5 authors

Following the recent trend of data-centric AI, we propose a clustering method to offer additional insight into precious and yet less-explored cryptocurrency price time series. While invaluable efforts have been conveyed in the domain of time series clustering, we integrate and harmonize some of the best practices in the field, namely Gramian Angular Field (GAF), Variational AutoEncoders (VAEs), and Deep Embedded Clustering (DEC). We use time series to image transformations as a preprocessing step for VAE to reduce dimensionality. After performing K-means clustering on VAE’s latent space, we provide DEC with cluster centroids from the previous step and retrain our network to do the clustering task. We evaluate the proposed method with the Bitcoin Tick-bar price dataset from 2017 onwards. Results demonstrate that our method leads to financially interpretable clusters and can improve Silhouette Score up to 10 percent compared to non-imaged time series.

Megan Czasonis, Mark Kritzman, Baykan Pamir, David Turkington

The role of cryptocurrencies as a vehicle for speculation has been well established. However, it is less clear if cryptocurrencies can also serve to manage risk. The authors seek to determine the diversification potential of cryptocurrencies both for short and long horizons. For short horizons, they estimate correlations that consider the direction and magnitude of returns for relevant asset classes, rather than focusing on full-sample correlations, as is customary. For long horizons, they compute “single period correlations” that capture the extent to which cryptocurrencies move synchronously with, or drift apart from, other assets over an investor’s horizon. They also identify utility-maximizing allocations to cryptocurrencies directly from historical return samples that account for all features of the data as well as more nuanced preferences than are typically assumed.

Pierluigi Freni, Enrico Ferro, Roberto Moncada

Blockchain technology has been around for more than ten years, nevertheless, the knowledge about its economic and business implications is still fragmented and heterogeneous. The present article intends to tackle this issue with a twofold contribution. The first is an analysis of the shift from economics to tokenomics highlighting the central role played by tokens within blockchain-based ecosystems. The second is a framework for tokens design leveraging a morphological analysis deeply grounded in the literature. As blockchain becomes a mainstream phenomenon, the value of the work proposed lies in lowering the cognitive barriers and in clarifying the space of available options for private and public actors willing to leverage tokenization in their daily operations.

Rohit Beer, Tarunim Sharma

Cryptocurrency, a name heard in the news as well as social media. But what exactly is cryptocurrency? Cryptocurrency is a decentralized digital asset on the blockchain, which means, that it is not controlled by an entity or an institution. Therefore, users of cryptocurrency enjoy financial freedom. But how are most of the transactions related to cryptocurrency processed on the blockchain? There is a term called proof of work as well as proof of stake. This paper has focused on the proof of work aspect of processing the cryptocurrency transactions, also specified the currencies that support the proof of work consensus. It also distinguishes between the two types of hardware that can be used to mine the currencies, and also using the past one-year data recorded by the author to analyze whether mining can be fruitful with any hardware in the future. Also, commenting on which type hardware can yield the best ROI. This type of thorough research on PoW consensus has not been found, so this paper is the first of its kind. A prediction has also been made using Python, whether mining in the near future will be of great potential or not. Also highlighted why mining revenue fluctuates.

Ahmad Fraz, Arshad Hassan, Sumayya Chughtai

The current study has examined the informational efficiency of market leader of cryptocurrency i.e, Bitcoin. The daily, weekly and monthly prices of Bitcoin have been used for analysis from 2013 to 2017. The information efficiency has been investigated by using different tests of random walk both parametric and non-parametric. The results indicate the Bitcoin returns are not weak form efficient and the element of random walk is not there. Hence, the investors have an opportunity to beat the market by using technical trading and get abnormal returns from the predictability of Bitcoin prices.

Klaus Grobys, Josephine Dufitinema, Niranjan Sapkota, James W. Kolari

In the era of digitalization, cryptocurrencies have become an alternative asset for both retail and institutional investors. While the emerging digital ecosystem based on blockchain technology offers numerous advantages, it is important to be aware of potential risks such as hacking incidents. In the 2011–2021 period, approximately 1.7 million units of Bitcoin were stolen due to criminal activity with losses exceeding $700 million. This paper models the distribution of stolen coins as a fractal process using power laws to estimate the expected losses from Bitcoin cyberattacks. Our results show that naïve statistics dramatically underestimate the expected loss by more than 70 percent. Our findings have important policy implications with respect to the urgent need for cryptocurrency market oversight by governments and regulatory agencies.

Bernhard K. Meister, Henry C. W. Price

The paper highlights some commonalities between the development of cryptocurrencies and the evolution of ecosystems. Concepts from evolutionary finance embedded in toy models consistent with stylized facts are employed to understand what survival of the fittest means in cryptofinance. Stylized facts for ownership, trading volume and market capitalization of cryptocurrencies are selectively presented in terms of scaling laws.

Zhengkun Li

Value at risk and expected shortfall are increasingly popular tail risk measures in the financial risk management field. Both academia and financial institutions are working to improve tail risk forecasts in order to meet the requirements of the Basel Capital Accord; it states that one purpose of risk management and measuring risk accuracy is, since extreme movements cannot always be avoided, financial institutions can prepare for these extreme returns by capital allocation, and putting aside the appropriate amount of capital so as to avoid default in times of extreme price or index movements. Forecast combination has drawn much attention, as a combined forecast can outperform the individual forecasts under certain conditions. We propose two methodology, one is a semiparametric combination framework that can jointly produce combined value at risk and expected shortfall forecasts, another one is a parametric regression framework named as Quantile-ES regression that can produce combined expected shortfall forecasts. The favourability of the semiparametric combination framework has been presented via an empirical study - application in cryptocurrency markets with high-frequency data where the necessity of risk management application increases as the cryptocurrency market becomes more popular and mature. Additionally, the general framework of the parametric Quantile-ES regression has been presented via a simulation study, whereas it still need to be improved in the future. The contributions of this work include but are not limited to the enabling of the combination of expected shortfall forecasts and the application of risk management procedures in the cryptocurrency market with high-frequency data.

Віктор Козюк

Cryptocurrencies show tremendous growth by market capitalization, however Bitcoin cross-country holdings are still in question. The purpose of the paper is to show that inflation discontent with the rule of law failures can explain why residents of different countries are prone to cryptocurrency holdings. The level of financial development is also considered. A hypothesis is proposed for more complex and segmented motives of Bitcoin holdings, tested by the OLS method. Single- and multi-factor regressions with independent variables are used, which can validate cross-country Bitcoin holdings in terms of inflation discontent, quality of institutions and financial development. Regression results confirm the idea of more segmented motives to hold Bitcoins. First, the hedge against inflation motive is rooted in the institutional weakness of central banks, and the regression results show that inflation variables are the most significant. Second, the hedge against institutional risks of asset ownership motive, based on the lack of rule of law and the relevant variable, is best performing among other institutional variables. Third, it is wrong to neglect financial development. However, it only plays a role in interaction with better innovation performance, meaning that crypto investors try not only to diversify their portfolios, but also to profit from involving in a sector with promising technological perspectives. The main takeaway is that institutional factors help explain why people in countries with worsened inflation and institutional performance tend to hold a large fraction of Bitcoins in assets. Obviously, monetary and institutional fragility is underestimated in the general discussion about the nature of digital money.