Mathematics, Cryptocurrencies and Blockchain Technology

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 59469

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Special Issue Editors

Faculty of Economic and Business Sciences, University of Extremadura, 06006 Badajoz, Spain
Interests: asset pricing; risk management; financial risk management; portfolio management; portfolio theory; portfolio optimization; portfolio risk measurement; financial econometrics; behavioral finance; financial crises
Special Issues, Collections and Topics in MDPI journals
Faculty of Economic and Business Sciences, University of Extremadura, 06006 Badajoz, Spain
Interests: financial analysis; asset pricing; portfolio; financial markets; portfolio management; finance; portfolio optimization; corporate finance; econometrics; investment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cryptocurrencies, which can be understood as mathematical money, are one of the main challenges that exist in the field of finance today and in our society and way of life in the very near future. The academic world cannot be oblivious to this development. That is why we propose to be Guest Editors of a Special Issue dedicated to the role of mathematics in the transformation of the world through these instruments. Our aim is to contribute to this Special Issue with our research and to encourage our national and international colleagues and the rest of the academic community to submit articles to the Special Issue. Cryptocurrencies are built using complex mathematics and computational methods. However, we do not focus only on mathematics as a feature of the security and effectiveness of cryptocurrencies protocols but also on the pricing mathematics. In that context, the pricing mathematics underlying these instruments can be useful tools for prediction or for estimating the reasonable value of something. Therefore, we consider that an adequate academic research in this field will reinforce the importance of mathematics for cryptocurrencies.

Potential topics include, but are not limited to:

  • Forecasting models
  • Effects on financial markets
  • Mathematical models
  • Cryptocurrencies mining
  • Future of cryptocurrencies
  • Abnormal volatility
  • Cryptocurrency pump and dump
  • Cybercrime and cryptocurrencies
  • Exchange frauds in digital transactions

Prof. Dr. José Luis Miralles-Quirós
Prof. Dr. Maria Del Mar Miralles-Quirós
Guest Editors

Manuscript Submission Information

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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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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
  • financial technology
  • mathematical models
  • cybercrime
  • volatility

Published Papers (10 papers)

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Editorial

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2 pages, 185 KiB  
Editorial
Mathematics, Cryptocurrencies and Blockchain Technology
by José Luis Miralles-Quirós and María Mar Miralles-Quirós
Mathematics 2022, 10(12), 2038; https://doi.org/10.3390/math10122038 - 12 Jun 2022
Cited by 1 | Viewed by 1137
Abstract
This book contains the successful invited submissions [...] Full article
(This article belongs to the Special Issue Mathematics, Cryptocurrencies and Blockchain Technology)

Research

Jump to: Editorial

33 pages, 630 KiB  
Article
The Macroeconomic Effects of an Interest-Bearing CBDC: A DSGE Model
by Ferry Syarifuddin and Toni Bakhtiar
Mathematics 2022, 10(10), 1671; https://doi.org/10.3390/math10101671 - 13 May 2022
Cited by 3 | Viewed by 4056
Abstract
We develop a medium size dynamic stochastic general equilibrium (DSGE) model to assess the macroeconomic consequences of introducing an interest-bearing central bank digital currency (CBDC), an electronic alternative of payment with public use properties of cash and that can furnish as bank settlement [...] Read more.
We develop a medium size dynamic stochastic general equilibrium (DSGE) model to assess the macroeconomic consequences of introducing an interest-bearing central bank digital currency (CBDC), an electronic alternative of payment with public use properties of cash and that can furnish as bank settlement balances. The model consists of seven sectors, namely households, retail firms, wholesale firms, capital producing firms, commercial banks, central bank, and government, and offers rich features. The use of cash and CBDC is differentiated with respect to their prices and transaction costs. In particular, we quantify the effects of negative shock on CBDC transaction cost to evaluate the potential of CBDC as an alternate instrument in liquidity holding in addition to cash and bank deposits. We also examine the effects of productivity shock and monetary policy shock on CBDC interest rate and CBDC demand, and their interaction with other main variables of the model. Full article
(This article belongs to the Special Issue Mathematics, Cryptocurrencies and Blockchain Technology)
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21 pages, 758 KiB  
Article
A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin
by Zi Ye, Yinxu Wu, Hui Chen, Yi Pan and Qingshan Jiang
Mathematics 2022, 10(8), 1307; https://doi.org/10.3390/math10081307 - 14 Apr 2022
Cited by 27 | Viewed by 5503
Abstract
Cryptocurrencies can be considered as mathematical money. As the most famous cryptocurrency, the Bitcoin price forecasting model is one of the popular mathematical models in financial technology because of its large price fluctuations and complexity. This paper proposes a novel ensemble deep learning [...] Read more.
Cryptocurrencies can be considered as mathematical money. As the most famous cryptocurrency, the Bitcoin price forecasting model is one of the popular mathematical models in financial technology because of its large price fluctuations and complexity. This paper proposes a novel ensemble deep learning model to predict Bitcoin’s next 30 min prices by using price data, technical indicators and sentiment indexes, which integrates two kinds of neural networks, long short-term memory (LSTM) and gate recurrent unit (GRU), with stacking ensemble technique to improve the accuracy of decision. Because of the real-time updates of comments on social media, this paper uses social media texts instead of news websites as the source data of public opinion. It is processed by linguistic statistical method to form the sentiment indexes. Meanwhile, as a financial market forecasting model, the model selects the technical indicators as input as well. Real data from September 2017 to January 2021 is used to train and evaluate the model. The experimental results show that the near-real time prediction has a better performance, with a mean absolute error (MAE) 88.74% better than the daily prediction. The purpose of this work is to explain our solution and show that the ensemble method has better performance and can better help investors in making the right investment decision than other traditional models. Full article
(This article belongs to the Special Issue Mathematics, Cryptocurrencies and Blockchain Technology)
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24 pages, 3383 KiB  
Article
Do Not Rug on Me: Leveraging Machine Learning Techniques for Automated Scam Detection
by Bruno Mazorra, Victor Adan and Vanesa Daza
Mathematics 2022, 10(6), 949; https://doi.org/10.3390/math10060949 - 16 Mar 2022
Cited by 15 | Viewed by 11373
Abstract
Uniswap, as with other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also make it easy to [...] Read more.
Uniswap, as with other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also make it easy to execute initial coin offering scams by listing non-valuable tokens. This method of performing scams is known as rug pull, a phenomenon that already exists in traditional finance but has become more relevant in DeFi. Various projects have contributed to detecting rug pulls in EVM compatible chains. However, the first longitudinal and academic step to detecting and characterizing scam tokens on Uniswap was made. The authors collected all the transactions related to the Uniswap V2 exchange and proposed a machine learning algorithm to label tokens as scams. However, the algorithm is only valuable for detecting scams accurately after they have been executed. This paper increases their dataset by 20K tokens and proposes a new methodology to label tokens as scams. After manually analyzing the data, we devised a theoretical classification of different malicious maneuvers in the Uniswap protocol. We propose various machine-learning-based algorithms with new, relevant features related to the token propagation and smart contract heuristics to detect potential rug pulls before they occur. In general, the models proposed achieved similar results. The best model obtained accuracy of 0.9936, recall of 0.9540, and precision of 0.9838 in distinguishing non-malicious tokens from scams prior to the malicious maneuver. Full article
(This article belongs to the Special Issue Mathematics, Cryptocurrencies and Blockchain Technology)
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15 pages, 4028 KiB  
Article
Is There an Asymmetric Relationship between Economic Policy Uncertainty, Cryptocurrencies, and Global Green Bonds? Evidence from the United States of America
by Aamir Aijaz Syed, Farhan Ahmed, Muhammad Abdul Kamal, Assad Ullah and Jose Pedro Ramos-Requena
Mathematics 2022, 10(5), 720; https://doi.org/10.3390/math10050720 - 24 Feb 2022
Cited by 30 | Viewed by 3580
Abstract
The environmental degradation and the concern for sustainable development have garnered extensive attention from researchers to evaluate the prospects of green bonds over other traditional assets. Against this backdrop, the current study measures the asymmetric relationship between green bonds, U.S. economic policy uncertainty [...] Read more.
The environmental degradation and the concern for sustainable development have garnered extensive attention from researchers to evaluate the prospects of green bonds over other traditional assets. Against this backdrop, the current study measures the asymmetric relationship between green bonds, U.S. economic policy uncertainty (EPU), and bitcoins by employing the Nonlinear Autoregressive Distribution Lag (NARDL) estimation technique recently developed by Shin et al. The outcome of the empirical analysis confirms an asymmetric cointegration between EPU, bitcoins, the clean energy index, oil prices, and green bonds. The NARDL estimation substantiates that positive shock in EPU exerts a negative impact on green bonds, whereas a negative shock in EPU increases the performance of green bonds. It implies, in the long run, a 1 percent increase (decrease) in EPU decreases (increases) the performance of green bonds by 0.22 percent and 0.11 percent, respectively. Likewise, the study also confirms a bidirectional relationship between bitcoins and green bonds. A positive shock in bitcoin increases the performance of green bonds and vice versa. In addition, our study also reveals a direct co-movement between clean energy, oil prices, and green bonds. This outcome implies that green bonds are not a different asset class, and they mirror the performance of other asset classes, such as clean energy, oil prices, and bitcoins. The findings offer several implications to understand the hedging and diversification properties of bitcoins, and assist in understanding the role of U.S. economic policy uncertainty on green bonds. Full article
(This article belongs to the Special Issue Mathematics, Cryptocurrencies and Blockchain Technology)
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24 pages, 521 KiB  
Article
A Dual Incentive Value-Based Paradigm for Improving the Business Market Profitability in Blockchain Token Economy
by Chaopeng Guo, Pengyi Zhang, Bangyao Lin and Jie Song
Mathematics 2022, 10(3), 439; https://doi.org/10.3390/math10030439 - 29 Jan 2022
Cited by 6 | Viewed by 2332
Abstract
Blockchain solves the problem of mutual trust and consensus in the business market of the token economy. In the existing paradigm of blockchain token economy, there are disadvantages of lacking the incentive mechanism, business applications and virtual token value. These shortcomings reduce consumers’ [...] Read more.
Blockchain solves the problem of mutual trust and consensus in the business market of the token economy. In the existing paradigm of blockchain token economy, there are disadvantages of lacking the incentive mechanism, business applications and virtual token value. These shortcomings reduce consumers’ willingness to consume and the profits of the merchants. In this paper, we propose a novel “Dual incentive value-based” paradigm to improve the business market profitability in blockchain token economy. To evaluate our proposed paradigm, we propose a business study case for improving merchants’ environment state. In this case, we set up two economic models and make simulations to validate the profitability. The result shows that merchants with the novel paradigm have 32% more profit compared with those without the paradigm and at most 10% more profitable than those in existing paradigms. Full article
(This article belongs to the Special Issue Mathematics, Cryptocurrencies and Blockchain Technology)
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13 pages, 1868 KiB  
Article
The NFT Hype: What Draws Attention to Non-Fungible Tokens?
by Christian Pinto-Gutiérrez, Sandra Gaitán, Diego Jaramillo and Simón Velasquez
Mathematics 2022, 10(3), 335; https://doi.org/10.3390/math10030335 - 22 Jan 2022
Cited by 61 | Viewed by 18185
Abstract
Non-fungible tokens (NFTs) can be used to represent ownership of digital art or any other unique digital item where ownership is recorded in smart contracts on a blockchain. NFTs have recently received enormous attention from both cryptocurrency investors and the media. We examine [...] Read more.
Non-fungible tokens (NFTs) can be used to represent ownership of digital art or any other unique digital item where ownership is recorded in smart contracts on a blockchain. NFTs have recently received enormous attention from both cryptocurrency investors and the media. We examine why NFTs have gotten so much attention. Using vector autoregressive models, we show that Bitcoin returns significantly predict next week’s NFT growth in popularity, measured by Google search queries. Moreover, wavelet coherence analysis suggests that Bitcoin and Ether returns are significant drivers of next week’s attention to NFTs. These results indicate that the remarkable increases in prices of major cryptocurrencies can explain the hype around NFTs. Full article
(This article belongs to the Special Issue Mathematics, Cryptocurrencies and Blockchain Technology)
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19 pages, 3718 KiB  
Article
Cryptocurrency as Epidemiologically Safe Means of Transactions: Diminishing Risk of SARS-CoV-2 Spread
by Dmitry V. Boguslavsky, Natalia P. Sharova and Konstantin S. Sharov
Mathematics 2021, 9(24), 3263; https://doi.org/10.3390/math9243263 - 15 Dec 2021
Cited by 6 | Viewed by 2589
Abstract
In comparison with other respiratory viruses, the current COVID-19 pandemic’s rapid seizing the world can be attributed to indirect (contact) way of transmission of SARS-CoV-2 virus in addition to the regular airborne way. A significant part of indirect transmission is made through cash [...] Read more.
In comparison with other respiratory viruses, the current COVID-19 pandemic’s rapid seizing the world can be attributed to indirect (contact) way of transmission of SARS-CoV-2 virus in addition to the regular airborne way. A significant part of indirect transmission is made through cash bank notes. SARS-CoV-2 remains on cash paper money for period around four times larger than influenza A virus and is absorbed by cash notes two and a half times more effectively than influenza A (our model). During the pandemic, cryptocurrencies have gained attractiveness as an “epidemiologically safe” means of transactions. On the basis of the authors’ gallop polls performed online with social networks users in 44 countries in 2020–2021 (the total number of clear responses after the set repair 32,115), around 14.7% of surveyed participants engaged in cryptocurrency-based transactions during the pandemic. This may be one of the reasons of significant rise of cryptocurrencies rates since mid-March 2020 till the end of 2021. The paper discusses the reasons for cryptocurrency attractiveness during the COVID-19 pandemic. Among them, there are fear of SARS-CoV-2 spread via cash contacts and the ability of the general population to mine cryptocurrencies. The article also provides a breakdown of the polled audience profile to determine the nationalities that have maximal level of trust to saving and transacting money as cryptocurrencies. Full article
(This article belongs to the Special Issue Mathematics, Cryptocurrencies and Blockchain Technology)
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10 pages, 1409 KiB  
Article
Trading Cryptocurrencies Using Second Order Stochastic Dominance
by Gil Cohen
Mathematics 2021, 9(22), 2861; https://doi.org/10.3390/math9222861 - 11 Nov 2021
Cited by 2 | Viewed by 2944
Abstract
This research is the first attempt to customize a trading system that is based on second order stochastic dominance (SSD) to five known cryptocurrencies’ daily data: Bitcoin, Ethereum, XRP, Binance Coin, and Cardano. Results show that our system can predict price trends of [...] Read more.
This research is the first attempt to customize a trading system that is based on second order stochastic dominance (SSD) to five known cryptocurrencies’ daily data: Bitcoin, Ethereum, XRP, Binance Coin, and Cardano. Results show that our system can predict price trends of cryptocurrencies, trade them profitably, and in most cases outperform the buy and hold (B&H) simple strategy. Our system’s best performance was achieved trading XRP, Binance Coin, Ethereum, and Bitcoin. Although our system has also generated a positive net profit (NP) for Cardano, it failed to outperform the B&H strategy. For all currencies, the system better predicted long trends than short trends. Full article
(This article belongs to the Special Issue Mathematics, Cryptocurrencies and Blockchain Technology)
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24 pages, 2537 KiB  
Article
Detecting Jump Risk and Jump-Diffusion Model for Bitcoin Options Pricing and Hedging
by Kuo-Shing Chen and Yu-Chuan Huang
Mathematics 2021, 9(20), 2567; https://doi.org/10.3390/math9202567 - 13 Oct 2021
Cited by 7 | Viewed by 2987
Abstract
In this paper, we conduct a fast calibration in the jump-diffusion model to capture the Bitcoin price dynamics, as well as the behavior of some components affecting the price itself, such as the risk of pitfalls and its ambiguous effect on the evolution [...] Read more.
In this paper, we conduct a fast calibration in the jump-diffusion model to capture the Bitcoin price dynamics, as well as the behavior of some components affecting the price itself, such as the risk of pitfalls and its ambiguous effect on the evolution of Bitcoin’s price. In addition, in our study of the Bitcoin option pricing, we find that the inclusion of jumps in returns and volatilities are significant in the historical time series of Bitcoin prices. The benefits of incorporating these jumps flow over into option pricing, as well as adequately capture the volatility smile in option prices. To the best of our knowledge, this is the first work to analyze the phenomenon of price jump risk and to interpret Bitcoin option valuation as “exceptionally ambiguous”. Crucially, using hedging options for the Bitcoin market, we also prove some important properties: Bitcoin options follow a convex, but not strictly convex function. This property provides adequate risk assessment for convex risk measure. Full article
(This article belongs to the Special Issue Mathematics, Cryptocurrencies and Blockchain Technology)
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