School of Economics, Hangzhou Dianzi University, Hangzhou, China.
Front Public Health. 2022 Feb 21;10:822097. doi: 10.3389/fpubh.2022.822097. eCollection 2022.
The rapid spread of COVID-19 worldwide makes an uncertain impact on the development of digital finance in China. In this background, the measurement of digital financial risk and analysis of influence factor become the focus of the financial field. Therefore, this article builds the indicator system of digital financial risk and uses the Lagrange multiplier method to obtain the optimal comprehensive weight of AHP and entropy weight. Then, this article measures the digital financial risk indexes of China's major regions with high-level economic development from 2013 to 2020. Furthermore, the maximum likelihood estimates of the unknown parameters of skew-normal panel data model are obtained based on the EM algorithm, and the comparative study of the normal and skew-normal panel data models is conducted under AIC and BIC. Finally, based on the result of the model, the influence factors of digital financial risk of China's economically developed regions under COVID-19 are analyzed to provide data support for the prevention and governance of digital financial risk.
新冠疫情在全球的迅速传播，对中国数字金融的发展产生了不确定影响。在此背景下，数字金融风险的度量和影响因素分析成为金融领域的关注焦点。因此，本文构建了数字金融风险指标体系，采用拉格朗日乘数法得到 AHP 和熵权的最优综合权重。然后，本文利用极大似然估计法（EM 算法）得到了 skewnormal 面板数据模型的未知参数的最大似然估计值，并基于 AIC 和 BIC 准则对正态和 skewnormal 面板数据模型进行了对比研究。最后，基于模型结果，分析了新冠疫情下中国经济发达地区数字金融风险的影响因素，为数字金融风险的防范和治理提供了数据支持。
