Author(s)

Yutong Xin

Corresponding Author

Yutong Xin

Abstract

Financial risk warning has profound theoretical and practical significance for maintaining the healthy development of the economy and ensuring the stable operation of the financial system. Finding a scientific, accurate, and efficient risk measurement method is crucial for improving risk management efficiency and ensuring financial security. In recent years, with the rapid improvement of computing technology and hardware performance, Bayesian statistical inference, as a powerful data analysis tool, has gradually emerged in the field of financial risk management and received increasing attention and application. The Bayesian method, by combining prior information with sample data, can more comprehensively reflect uncertainty and provide a more robust and reliable basis for risk prediction. This article delves into the specific applications of Bayesian based statistical forecasting in financial data analysis. By constructing Bayesian models, we can quantitatively analyze various risks in the financial market. Meanwhile, Bayesian methods can effectively handle complex features such as nonlinearity and non stationarity in financial data, improving the accuracy and timeliness of risk warning.

Keywords

Bayesian method; Statistical prediction; Financial data analysis