 Research on Asset Allocation and Risk Management of Digital Cryptocurrencies Based on the GARCH ModelYihao Liu ()
A chapter in Proceedings of the 2025 10th International Conference on Financial Innovation and Economic Development (ICFIED 2025), 2025, pp 405-418 from Springer Abstract: Abstract With the first wave of technological and industrial revolutions progressing at full steam, the global economy has undergone further in-depth adjustments. China’s financial system has now been further improved, with a particular emphasis on digitalization, intelligence, and diversification. Digital cryptocurrencies, as a trading medium that ensures transaction security and controls the units of transaction, have gradually entered people’s field of vision due to their decentralized characteristics. However, with the further increase in the popularity of the digital cryptocurrency trading market, financial investors have experienced multiple large amplitude fluctuations in the digital cryptocurrency market. The high risk behind the high returns has gradually become the topic of discussion among investors. Therefore, the study of asset allocation and risk management in the digital cryptocurrency market aims to quantify the trading patterns of the digital cryptocurrency market and provide feasible investment schemes for investors. However, at present in China, there is a scarcity of research on asset allocation and risk management related to digital cryptocurrencies, and the models and research subjects used are relatively singular. The reason is that the popularity and attention to digital cryptocurrencies in the public are lower, and the number of investors is much less than that of common financial assets and their derivatives such as stocks and funds. This paper aims to use statistical models, selecting four types of digital cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), USDT, and USDC for research, utilizing the GARCH (p, q) model to fit the logarithmic return of digital cryptocurrencies. It calculates the Value at Risk (VaR) and Conditional Value at Risk (CVaR) for these four types of digital currencies and different investment portfolios. It also analyzes the correlation within different investment portfolios. The results show that: (1) the GARCH(1,1) model is better than the GARCH (0,1) model; (2) the return fluctuation range of these four types of digital cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), USDT, and USDC is relatively large; (3) holding a large amount of Bitcoin and Ethereum in the portfolio may increase the risk exposure of the portfolio, while USDC/USDT is usually pegged to the US dollar and its price is relatively stable. Therefore, the low correlation between Bitcoin and USDC may help to reduce the overall risk of the investment portfolio. In summary, the research on asset allocation and risk management of digital cryptocurrencies based on the GARCH model will help to popularize and increase public attention to digital cryptocurrency investments, and it will provide a good supplement to the content of risk management of GARCH and other financial mathematical models in financial and derivative products. This research will provide optimal investment advice to digital cryptocurrency investors in the rapidly changing market, and it will directly help the trading behavior of investors. Its practical significance will also have a certain role in promoting the development of the digital cryptocurrency industry. Keywords: Digital Cryptocurrencies; Asset Allocation; Risk Management; GARCH Model; Value at Risk; Conditional Value at Risk (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-702-1_44 Ordering information: This item can be ordered from DOI: 10.2991/978-94-6463-702-1_44 Access Statistics for this chapter More chapters in Advances in Economics, Business and Management Research from Springer |
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