Topical Collection "Blockchain and Cryptocurrencies"

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Editors

Dr. Saralees Nadarajah
E-Mail Website
Guest Editor
School of Mathematics, University of Manchester, Manchester M13 9PL, UK
Interests: extreme value theory and its applications; distribution theory; nonparametric statistics; information theory; reliability; sampling theory; statistical software; time series
Special Issues and Collections in MDPI journals
Dr. Stephen Chan
E-Mail Website
Guest Editor
Department of Mathematics and Statistics, American University of Sharjah, 26666 Sharjah, UAE
Interests: Extreme Value Analysis and Distribution Theory in analysing financial commodities data and cryptocurrency data, and Financial Risk models
Special Issues and Collections in MDPI journals
Dr. Jeffrey Chu
E-Mail Website
Guest Editor
Department of Statistics, Universidad Carlos III de Madrid, 28903 Getafe, Madrid, Spain
Interests: statistics and distribution theory with financial applications; cryptocurrencies; blockchain and social networks
Special Issues and Collections in MDPI journals
Dr. Yuanyuan Zhang
E-Mail Website
Guest Editor
School of Mathematics, University of Manchester, Manchester M13 9PL, UK
Interests: multivariate and extreme value analysis; big data sets; cryptocurrencies
Special Issues and Collections in MDPI journals

Topical Collection Information

Dear Colleagues,

Blockchain and cryptocurrencies have recently captured the interest of academics and those in industry. Cryptocurrencies are essentially digital currencies that use blockchain technology and cryptography to facilitate secure and anonymous transactions. The cryptocurrency market is currently worth over $500 billion. Many institutions and countries are starting to understand and implement the idea of cryptocurrencies in their business models. The aim of this Special Issue is to provide a collection of papers from leading experts in the area of blockchain and cryptocurrencies.

The topics covered in this Special Issue will include, but are not limited to:

  • Academic research on blockchain and cryptocurrencies
  • Industrial applications of blockchain and cryptocurrencies
  • Applications of fintech in academia and industry
  • The economics of blockchain technology
  • Financial analysis and risk management with cryptocurrencies

Dr. Saralees Nadarajah
Dr. Stephen Chan
Dr. Jeffrey Chu
Dr. Yuanyuan Zhang
Guest Editors

Manuscript Submission Information

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Keywords

  • Blockchain
  • Cryptocurrencies
  • Digital currencies
  • Fintech
  • Tokenization
  • Risk management
  • Financial analysis

Related Special Issue

Published Papers (10 papers)

2020

Jump to: 2019, 2018

Editorial
Blockchain and Cryptocurrencies
J. Risk Financial Manag. 2020, 13(10), 227; https://doi.org/10.3390/jrfm13100227 - 26 Sep 2020
Cited by 2 | Viewed by 1244
Abstract
Cryptocurrencies are essentially digital currencies that use blockchain technology and cryptography to facilitate secure and anonymous transactions. Many institutions and countries are starting to understand and implement the idea of cryptocurrencies in their business models. With this recent surge in interest, we believe [...] Read more.
Cryptocurrencies are essentially digital currencies that use blockchain technology and cryptography to facilitate secure and anonymous transactions. Many institutions and countries are starting to understand and implement the idea of cryptocurrencies in their business models. With this recent surge in interest, we believe that now is the time to start studying these areas as a key piece of financial technology. The aim of this Special Issue is to provide a collection of papers from leading experts in the area of blockchain and cryptocurrencies. The topics covered in this Special Issue includes the economics, financial analysis and risk management with cryptocurrencies. Full article
Article
Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables
J. Risk Financial Manag. 2020, 13(9), 189; https://doi.org/10.3390/jrfm13090189 - 19 Aug 2020
Cited by 7 | Viewed by 1574
Abstract
The Bitcoin (BTC) market presents itself as a new unique medium currency, and it is often hailed as the “currency of the future”. Simulating the BTC market in the price discovery process presents a unique set of market mechanics. The supply of BTC [...] Read more.
The Bitcoin (BTC) market presents itself as a new unique medium currency, and it is often hailed as the “currency of the future”. Simulating the BTC market in the price discovery process presents a unique set of market mechanics. The supply of BTC is determined by the number of miners and available BTC and by scripting algorithms for blockchain hashing, while both speculators and investors determine demand. One major question then is to understand how BTC is valued and how different factors influence it. In this paper, the BTC market mechanics are broken down using vector autoregression (VAR) and Bayesian vector autoregression (BVAR) prediction models. The models proved to be very useful in simulating past BTC prices using a feature set of exogenous variables. The VAR model allows the analysis of individual factors of influence. This analysis contributes to an in-depth understanding of what drives BTC, and it can be useful to numerous stakeholders. This paper’s primary motivation is to capitalize on market movement and identify the significant price drivers, including stakeholders impacted, effects of time, as well as supply, demand, and other characteristics. The two VAR and BVAR models are compared with some state-of-the-art forecasting models over two time periods. Experimental results show that the vector-autoregression-based models achieved better performance compared to the traditional autoregression models and the Bayesian regression models. Full article
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Review
A Cryptocurrency Spectrum Short Analysis
J. Risk Financial Manag. 2020, 13(8), 184; https://doi.org/10.3390/jrfm13080184 - 17 Aug 2020
Cited by 1 | Viewed by 1464
Abstract
Technological development brings about economic changes that affect most citizens, both in developed and undeveloped countries. The implementation of blockchain technologies that bring cryptocurrencies into the economy and everyday life also induce risks. Authorities are continuously concerned about ensuring balance, which is, among [...] Read more.
Technological development brings about economic changes that affect most citizens, both in developed and undeveloped countries. The implementation of blockchain technologies that bring cryptocurrencies into the economy and everyday life also induce risks. Authorities are continuously concerned about ensuring balance, which is, among other things, a prudent attitude. Achieving this goal sometimes requires the development of standards and regulations applicable at the national or global level. This paper attempts to dive deeper into the worldwide operations, related to cryptocurrencies, as part of a general phenomenon, and also expose some of the intersections with cybercrime. Without impeding creativity, implementing suggested proposals must comply with the rules in effect and provide sufficient flexibility for adapting and integrating them. Different segments need to align or reposition, as alteration is only allowed in a positive way. Adopting cryptocurrency decisions should be unitary, based on standard policies. Full article
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Article
On the Market Efficiency and Liquidity of High-Frequency Cryptocurrencies in a Bull and Bear Market
J. Risk Financial Manag. 2020, 13(1), 8; https://doi.org/10.3390/jrfm13010008 - 03 Jan 2020
Cited by 7 | Viewed by 1909
Abstract
The market for cryptocurrencies has experienced extremely turbulent conditions in recent times, and we can clearly identify strong bull and bear market phenomena over the past year. In this paper, we utilise algorithms for detecting turnings points to identify both bull and bear [...] Read more.
The market for cryptocurrencies has experienced extremely turbulent conditions in recent times, and we can clearly identify strong bull and bear market phenomena over the past year. In this paper, we utilise algorithms for detecting turnings points to identify both bull and bear phases in high-frequency markets for the three largest cryptocurrencies of Bitcoin, Ethereum, and Litecoin. We also examine the market efficiency and liquidity of the selected cryptocurrencies during these periods using high-frequency data. Our findings show that the hourly returns of the three cryptocurrencies during a bull market indicate market efficiency when using the detrended-fluctuation-analysis (DFA) method to analyse the Hurst exponent with a rolling window. However, when conditions turn and there is a bear-market period, we see signs of a more inefficient market. Furthermore, our results indicated differences between the cryptocurrencies in terms of their liquidity during the two market states. Moving from a bull to a bear market, Ethereum and Litecoin appear to become more illiquid, as opposed to Bitcoin, which appears to become more liquid. The motivation to study the high-frequency cryptocurrency market came from the increasing availability of higher-frequency cryptocurrency-pricing data. However, it also comes from a movement towards higher-frequency trading of cryptocurrency. In addition, the efficiency of cryptocurrency markets relates not only to whether prices are predictable and arbitrage opportunities exist, but, more widely, to topics such as testing the profitability of trading strategies and determining the maturity of cryptocurrency markets. Full article
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2019

Jump to: 2020, 2018

Article
Which Cryptocurrencies Are Mostly Traded in Distressed Times?
J. Risk Financial Manag. 2019, 12(3), 135; https://doi.org/10.3390/jrfm12030135 - 20 Aug 2019
Cited by 7 | Viewed by 1471
Abstract
This paper investigates the level of liquidity of digital currencies during the very intense bearish phase in their markets. The data employed span the period from April 2018 until January 2019, which is the second phase of bearish times with almost constant decreases. [...] Read more.
This paper investigates the level of liquidity of digital currencies during the very intense bearish phase in their markets. The data employed span the period from April 2018 until January 2019, which is the second phase of bearish times with almost constant decreases. The Amihud’s illiquidity ratio is employed in order to measure the liquidity of these digital assets. Findings indicate that the most popular cryptocurrencies exhibit higher levels of liquidity during stressed periods. Thereby, it is revealed that investors’ preferences for trading during highly risky times are favorable for well-known virtual currencies in the detriment of less-known ones. This enhances findings of relevant literature about strong and persistent positive or negative herding behavior of investors based on Bitcoin, Ethereum and highly-capitalized cryptocurrencies in general. Notably though, a tendency towards investing in the TrueUSD stablecoin has also emerged. Full article
Communication
Contagion Effect in Cryptocurrency Market
J. Risk Financial Manag. 2019, 12(3), 115; https://doi.org/10.3390/jrfm12030115 - 10 Jul 2019
Cited by 12 | Viewed by 2373
Abstract
The rapid development of cryptocurrencies has drawn attention to this particular market, with investors trying to understand its behaviour and researchers trying to explain it. The evolution of cryptocurrencies’ prices showed a kind of bubble and a crash at the end of 2017. [...] Read more.
The rapid development of cryptocurrencies has drawn attention to this particular market, with investors trying to understand its behaviour and researchers trying to explain it. The evolution of cryptocurrencies’ prices showed a kind of bubble and a crash at the end of 2017. Based on this event, and on the fact that Bitcoin is the most recognized cryptocurrency, we propose to evaluate the contagion effect between Bitcoin and other major cryptocurrencies. Using the Detrended Cross-Correlation Analysis correlation coefficient (ΔρDCCA) and comparing the period after and before the crash, we found evidence of a contagion effect, with this particular market being more integrated now than in the past—something that should be taken into account by current and potential investors. Full article
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Article
Next-Day Bitcoin Price Forecast
J. Risk Financial Manag. 2019, 12(2), 103; https://doi.org/10.3390/jrfm12020103 - 20 Jun 2019
Cited by 21 | Viewed by 5310
Abstract
This study analyzes forecasts of Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and without re-estimation of the forecast model for each step. For [...] Read more.
This study analyzes forecasts of Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and without re-estimation of the forecast model for each step. For cross-validation of forecast results, we consider two different training and test samples. In the first training-sample, NNAR performs better than ARIMA, while ARIMA outperforms NNAR in the second training-sample. Additionally, ARIMA with model re-estimation at each step outperforms NNAR in the two test-sample forecast periods. The Diebold Mariano test confirms the superiority of forecast results of ARIMA model over NNAR in the test-sample periods. Forecast performance of ARIMA models with and without re-estimation are identical for the estimated test-sample periods. Despite the sophistication of NNAR, this paper demonstrates ARIMA enduring power of volatile Bitcoin price prediction. Full article
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Review
A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets
J. Risk Financial Manag. 2019, 12(2), 67; https://doi.org/10.3390/jrfm12020067 - 18 Apr 2019
Cited by 31 | Viewed by 4126
Abstract
This study conducts a systematic survey on whether the pricing behavior of cryptocurrencies is predictable. Thus, the Efficient Market Hypothesis is rejected and speculation is feasible via trading. We center interest on the Rescaled Range (R/S) and Detrended Fluctuation Analysis (DFA) as well [...] Read more.
This study conducts a systematic survey on whether the pricing behavior of cryptocurrencies is predictable. Thus, the Efficient Market Hypothesis is rejected and speculation is feasible via trading. We center interest on the Rescaled Range (R/S) and Detrended Fluctuation Analysis (DFA) as well as other relevant methodologies of testing long memory in returns and volatility. It is found that the majority of academic papers provides evidence for inefficiency of Bitcoin and other digital currencies of primary importance. Nevertheless, large steps towards efficiency in cryptocurrencies have been traced during the last years. This can lead to less profitable trading strategies for speculators. Full article
Article
Spillover Risks on Cryptocurrency Markets: A Look from VAR-SVAR Granger Causality and Student’s-t Copulas
J. Risk Financial Manag. 2019, 12(2), 52; https://doi.org/10.3390/jrfm12020052 - 01 Apr 2019
Cited by 33 | Viewed by 3856
Abstract
This paper contributes a shred of quantitative evidence to the embryonic literature as well as existing empirical evidence regarding spillover risks among cryptocurrency markets. By using VAR (Vector Autoregressive Model)-SVAR (Structural Vector Autoregressive Model) Granger causality and Student’s-t Copulas, we find that Ethereum [...] Read more.
This paper contributes a shred of quantitative evidence to the embryonic literature as well as existing empirical evidence regarding spillover risks among cryptocurrency markets. By using VAR (Vector Autoregressive Model)-SVAR (Structural Vector Autoregressive Model) Granger causality and Student’s-t Copulas, we find that Ethereum is likely to be the independent coin in this market, while Bitcoin tends to be the spillover effect recipient. Our study sheds further light on investigating the contagion risks among cryptocurrencies by employing Student’s-t Copulas for joint distribution. This result suggests that all coins negatively change in terms of extreme value. The investors are advised to pay more attention to ‘bad news’ and moving patterns in order to make timely decisions on three types (buy, hold, and sell). Full article

2018

Jump to: 2020, 2019

Article
Are There Any Volatility Spill-Over Effects among Cryptocurrencies and Widely Traded Asset Classes?
J. Risk Financial Manag. 2018, 11(4), 66; https://doi.org/10.3390/jrfm11040066 - 23 Oct 2018
Cited by 18 | Viewed by 1936
Abstract
In the present paper, we investigate connectedness within cryptocurrency markets as well as across the Bitcoin index (hereafter, BPI) and widely traded asset classes such as traditional currencies, stock market indices and commodities, such as gold and Brent oil. A spill over index [...] Read more.
In the present paper, we investigate connectedness within cryptocurrency markets as well as across the Bitcoin index (hereafter, BPI) and widely traded asset classes such as traditional currencies, stock market indices and commodities, such as gold and Brent oil. A spill over index approach with the spectral representation of variance decomposition networks, is employed to measure connectedness. Results show no significant spillover effects between the nascent market of cryptocurrencies and other financial markets. We suggest that cryptocurrencies are real independent financial instruments that pose no danger to financial system stability. Concerning the connectedness within the cryptocurrency markets, we report a time–frequency–dynamics connectedness nature. Moreover, the decomposition of the total spill over index is mostly dominated by a short frequency component (2–4 days) leading to the conclusion that this nascent market is highly speculative at present. These findings provide insights for regulators and potential international investors. Full article
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