Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model
Article
Article Title | Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model |
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Article Category | Article |
Authors | Huang, Yongrong, Wang, Huiqing, Chen, Zhide, Feng, Chen, Zhu, Kexin, Yang, Xu and Yang, Wencheng |
Journal Title | IEEE Open Journal of the Computer Society |
Journal Citation | 5, pp. 83-94 |
Number of Pages | 12 |
Year | 2024 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 2644-1268 |
2644-125X | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/OJCS.2024.3370603 |
Web Address (URL) | https://ieeexplore.ieee.org/abstract/document/10449426 |
Abstract | Cryptocurrency, a novel digital asset within the blockchain technology ecosystem, has recently garnered significant attention in the investment world. Despite its growing popularity, the inherent volatility and instability of cryptocurrency investments necessitate a thorough risk evaluation. This study utilizes the Autoregressive Moving Average (ARMA) model combined with the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model to analyze the volatility of three major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)—over a period from January 1, 2017, to October 29, 2022. The dataset comprises daily closing prices, offering a comprehensive view of the market's fluctuations. Our analysis revealed that the value-at-risk (VaR) curves for these cryptocurrencies demonstrate significant volatility, encompassing a broad spectrum of returns. The overall risk profile is relatively high, with ETH exhibiting the highest risk, followed by BTC and BNB. The ARMA-GARCH-VaR model has proven effective in quantifying and assessing the market risks associated with cryptocurrencies, providing valuable insights for investors and policymakers in navigating the complex landscape of digital assets. |
Keywords | GARCH; VaR; market risk; cryptocurrency; data analysis |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460403. Data security and protection |
Byline Affiliations | Fujian Normal University, China |
Fuzhou Polytechnic, China | |
National Sun Yat-Sen University, Taiwan | |
Minjiang University, China | |
School of Mathematics, Physics and Computing |
https://research.usq.edu.au/item/z5y6w/evaluating-cryptocurrency-market-risk-on-the-blockchain-an-empirical-study-using-the-arma-garch-var-model
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Evaluating_Cryptocurrency_Market_Risk_on_the_Blockchain_An_Empirical_Study_Using_the_ARMA-GARCH-VaR_Model.pdf | ||
License: CC BY-NC-ND 4.0 | ||
File access level: Anyone |
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