Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model

Article


Huang, Yongrong, Wang, Huiqing, Chen, Zhide, Feng, Chen, Zhu, Kexin, Yang, Xu and Yang, Wencheng. 2024. "Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model." IEEE Open Journal of the Computer Society. 5, pp. 83-94. https://doi.org/10.1109/OJCS.2024.3370603
Article Title

Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model

Article CategoryArticle
AuthorsHuang, Yongrong, Wang, Huiqing, Chen, Zhide, Feng, Chen, Zhu, Kexin, Yang, Xu and Yang, Wencheng
Journal TitleIEEE Open Journal of the Computer Society
Journal Citation5, pp. 83-94
Number of Pages12
Year2024
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN2644-1268
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.

KeywordsGARCH; VaR; market risk; cryptocurrency; data analysis
ANZSRC Field of Research 2020460403. Data security and protection
Byline AffiliationsFujian Normal University, China
Fuzhou Polytechnic, China
National Sun Yat-Sen University, Taiwan
Minjiang University, China
School of Mathematics, Physics and Computing
Permalink -

https://research.usq.edu.au/item/z5y6w/evaluating-cryptocurrency-market-risk-on-the-blockchain-an-empirical-study-using-the-arma-garch-var-model

  • 5
    total views
  • 1
    total downloads
  • 1
    views this month
  • 1
    downloads this month

Export as

Related outputs

UAV Control Method Combining Reptile Meta-Reinforcement Learning and Generative Adversarial Imitation Learning
Jiang, Shui, Ge, Yanning, Yang, Xu, Yang, Wencheng and Cui, Hui. 2024. "UAV Control Method Combining Reptile Meta-Reinforcement Learning and Generative Adversarial Imitation Learning." Future Internet. 16 (3). https://doi.org/10.3390/fi16030105
Feature extraction and learning approaches for cancellable biometrics: A survey
Yang, Wencheng, Wang, Song, Hu, Jiankun, Tao, Xiaohui and Li, Yan. 2024. "Feature extraction and learning approaches for cancellable biometrics: A survey." CAAI Transactions on Intelligence Technology. 9 (1), pp. 4-25. https://doi.org/10.1049/cit2.12283
An Adaptive Feature Fusion Network for Alzheimer’s Disease Prediction
Wei, Shicheng, Li, Yan and Yang, Wencheng. 2023. "An Adaptive Feature Fusion Network for Alzheimer’s Disease Prediction." 12th International Conference on Health Information Science (HIS 2023). Melbourne, Australia 23 - 24 Oct 2023 Germany. https://doi.org/10.1007/978-981-99-7108-4
A Review of Homomorphic Encryption for Privacy-Preserving Biometrics
Yang, Wencheng, Wang, Song, Cui, Hui, Tang, Zhaohui and Li, Yan. 2023. "A Review of Homomorphic Encryption for Privacy-Preserving Biometrics." Sensors. 23 (7). https://doi.org/10.3390/s23073566
Hybrid KD-NFT: A multi-layered NFT assisted robust Knowledge Distillation framework for Internet of Things
Wang, Nai, Chen, Junjun, Wu, Di, Yang, Wencheng, Xiang, Yong and Sajjanhar, Atul. 2023. "Hybrid KD-NFT: A multi-layered NFT assisted robust Knowledge Distillation framework for Internet of Things." Journal of Information Security and Applications. 75. https://doi.org/10.1016/j.jisa.2023.103483
A review of multi-factor authentication in the Internet of Healthcare Things
Suleski, Tance, Ahmed, Mohiuddin, Yang, Wencheng and Wang, Eugene. 2023. "A review of multi-factor authentication in the Internet of Healthcare Things." Digital Health. 9, pp. 1-20. https://doi.org/10.1177/20552076231177144
Token-Based Biometric Enhanced Key Derivation for Authentication Over Wireless Networks
Cui, Hui, Yang, Xuechao, Yang, Wencheng, Qin, Baodong and Yi, Xun. 2023. "Token-Based Biometric Enhanced Key Derivation for Authentication Over Wireless Networks." IEEE Transactions on Network Science and Engineering. 10 (4), pp. 2347-2357. https://doi.org/10.1109/TNSE.2023.3246439
A Secure Online Fingerprint Authentication System for Industrial IoT Devices over 5G Networks
Bedari, Aseel, Wang, Song and Yang, Wencheng. 2022. "A Secure Online Fingerprint Authentication System for Industrial IoT Devices over 5G Networks." Sensors. 22 (19), pp. 1-16. https://doi.org/10.3390/s22197609
Multimedia security and privacy protection in the internet of things: research developments and challenges
Yang, Wencheng, Wang, Song, Hu, Jiankun and Karie, Nickson M.. 2022. "Multimedia security and privacy protection in the internet of things: research developments and challenges." International Journal of Multimedia Intelligence and Security. 4 (1), pp. 20-46. https://doi.org/10.1504/ijmis.2022.121282
A linear convolution-based cancelable fingerprint biometric authentication system
Yang, Wencheng, Wang, Song, Kang, James Jin, Johnstone, Michael N. and Bedari, Aseel. 2022. "A linear convolution-based cancelable fingerprint biometric authentication system." Computers and Security. 114, pp. 1-14. https://doi.org/10.1016/j.cose.2021.102583
A Review on Security Issues and Solutions of the Internet of Drones
Yang, Wencheng, Wang, Song, Yin, Xuefei, Wang, Xu and Hu, Jiankun. 2022. "A Review on Security Issues and Solutions of the Internet of Drones." IEEE Open Journal of the Computer Society. 3, pp. 96-110. https://doi.org/10.1109/OJCS.2022.3183003
Network Forensics in the Era of Artificial Intelligence
Yang, Wencheng, Johnstone, Michael N., Wang, Song, Karie, Nickson M., Bin Sahri, Nor Masri and Kang, James Jin. 2022. "Network Forensics in the Era of Artificial Intelligence." Ahmed, Mohiuddin, Islam, Sheikh Rabiul, Anwar, Adnan, Moustafa, Nour and Pathan, Al-Sakib Khan (ed.) Explainable Artificial Intelligence for Cyber Security: Next Generation Artificial Intelligence. Cham, Switzerland. Springer. pp. 171-190
Leveraging Artificial Intelligence Capabilities for Real-Time Monitoring of Cybersecurity Threats
Karie, Nickson M., Bin Sahri, Nor Masri Bin, Yang, Wencheng and Johnstone, Michael N.. 2022. "Leveraging Artificial Intelligence Capabilities for Real-Time Monitoring of Cybersecurity Threats." Ahmed, Mohiuddin, Islam, Sheikh Rabiul, Anwar, Adnan, Moustafa, Nour and Pathan, Al-Sakib Khan (ed.) Explainable Artificial Intelligence for Cyber Security: Next Generation Artificial Intelligence. Cham, Switzerland. Springer. pp. 141-169