Cryptocurrency Volatility and Its Impact on Emerging Markets: Quantitative Analysis

Cryptocurrency, a booming decentralised asset designed based on the blockchain architecture, is particularly important to the market at the present time by studying the volatility risk of cryptocurrencies. In this paper, we empirically analyse the volatility risk of cryptocurrencies through quantitative analysis models, comprehensively using the Markov state transition GARCH model with skewed distribution (Skew-MSGARCH) and the autoregressive conditional volatility density ARJI model introducing the Poisson jump factor, and selecting the earliest developed and the most mature currency price volatility daily return series, to deeply explore the volatility risk of digital cryptocurrencies. risk. Finally, it can be seen through in-depth analyses that the expectation factor and information inducement are the main reasons leading to the exacerbation of the volatility risk of digital cryptocurrencies. It is recommended that this situation be optimised and improved in terms of the value function of digital cryptocurrencies themselves and the implementation of systematic risk management and regulatory innovation.As an important component of the digital economy, blockchain technology can effectively regulate and improve the volatility of digital cryptocurrencies under macroeconomic policies, thereby maintaining the security and stability of emerging financial markets.

Chen, Y. (2021). Research on the impact of investor sentiment on the yield and fluctuation of cryptocurrency. Xinjiang University of Finance and Economics.

Jia, B. X., Shen, D. H., & Zhang, W. (2023). Research on the performance of trend timing strategy based on cryptocurrency market. Systems Science and Mathematics, 43(9), 2266-2283. DOI: 10. 12341/jssms22863.

Lu, Y. M. (2020). Research on price fluctuation of digital cryptocurrency—Based on Bitcoin. Public Investment Guide.

Qiu, Y., & Xie, T. (2024). Exploring model uncertainty in cryptocurrency volatility forecasting: Log conversion, time zone sampling, and model setting. Systems Science and Mathematics, 44(3), 824-843. DOI: 10. 12341/jssms23304.