Affiliations: [a] School of Finance, Nanjing University of Finance & Economics, Nanjing, China
| [b] Department of Financial Engineering, Business School, Quanzhou Normal University, China
Correspondence:
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Corresponding author. Huang Shiwang, Department of Financial Engineering, Business School, Quanzhou Normal University, China. E-mail: iseusa@sina.com.
Abstract: The various parts of the traditional financial supervision and management system can no longer meet the current needs, and further improvement is urgently needed. In this paper, the low-frequency data is regarded as the missing of the high-frequency data, and the mixed frequency VAR model is adopted. In order to overcome the problems caused by too many parameters of the VAR model, this paper adopts the Bayesian estimation method based on the Minnesota prior to obtain the posterior distribution of each parameter of the VAR model. Moreover, this paper uses methods based on Kalman filtering and Kalman smoothing to obtain the posterior distribution of latent state variables. Then, according to the posterior distribution of the VAR model parameters and the posterior distribution of the latent state variables, this paper uses the Gibbs sampling method to obtain the mixed Bayes vector autoregressive model and the estimation of the state variables. Finally, this article studies the influence of Internet finance on monetary policy with examples. The research results show that the method proposed in this article has a certain effect.
Keywords: Autovector regression, internet finance, monetary policy, influence model
