Special Issue "Blockchain and Cryptocurrencies"

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Technology and Innovation".

Deadline for manuscript submissions: 31 August 2019

Special Issue Editors

Guest Editor
Dr. Saralees Nadarajah

School of Mathematics, University of Manchester, Manchester M13 9PL, UK
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Interests: extreme value theory and its applications; distribution theory; nonparametric statistics; information theory; reliability; sampling theory; statistical software; time series
Guest Editor
Dr. Stephen Chan

Department of Mathematics and Statistics, American University of Sharjah, UAE
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Interests: Extreme Value Analysis and Distribution Theory in analysing financial commodities data and cryptocurrency data, and Financial Risk models
Guest Editor
Dr. Jeffrey Chu

Department of Statistics, Universidad Carlos III de Madrid, 28903 Getafe, Madrid, Spain
Website | E-Mail
Interests: statistics and distribution theory with financial applications, cryptocurrencies, blockchain and social networks
Guest Editor
Dr. Yuanyuan Zhang

School of Mathematics, University of Manchester, Manchester M13 9PL, UK
Website | E-Mail
Interests: Multivariate and extreme value analysis, big data sets, cryptocurrencies

Special Issue 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. This Special Issue is also related to the conference entitled “Mathematics for Industry—Blockchain and Cryptocurrencies” https://blockchain-mcr.github.io/ . 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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (4 papers)

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Research

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Open AccessArticle
Next-Day Bitcoin Price Forecast
J. Risk Financial Manag. 2019, 12(2), 103; https://doi.org/10.3390/jrfm12020103
Received: 30 April 2019 / Revised: 10 June 2019 / Accepted: 14 June 2019 / Published: 20 June 2019
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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
(This article belongs to the Special Issue Blockchain and Cryptocurrencies)
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Open AccessArticle
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
Received: 1 March 2019 / Revised: 21 March 2019 / Accepted: 21 March 2019 / Published: 1 April 2019
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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
(This article belongs to the Special Issue Blockchain and Cryptocurrencies)
Open AccessArticle
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
Received: 4 September 2018 / Revised: 14 October 2018 / Accepted: 18 October 2018 / Published: 23 October 2018
Cited by 2 | PDF Full-text (596 KB) | HTML Full-text | XML Full-text
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
(This article belongs to the Special Issue Blockchain and Cryptocurrencies)
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Review

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Open AccessReview
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
Received: 2 April 2019 / Revised: 14 April 2019 / Accepted: 15 April 2019 / Published: 18 April 2019
Cited by 1 | PDF Full-text (367 KB) | HTML Full-text | XML Full-text
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
(This article belongs to the Special Issue Blockchain and Cryptocurrencies)
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