关键词: Ologit概率模型 , 省域金融风险 , 模型预警 , 先行指标

Abstract: Because of the extremely unbalanced development of China's regional economy, some provinces have great influence on the overall financial security of China, making it very important for the provincial and national financial security to pay attention to the risks of the provincial financial system and to carry out early warning and supervision. Under the background of further opening up of the financial market, this paper builds the leading early warning indicator system and the financial risk stress index of provincial financial risks in China. Using the provincial monthly sample data of Zhejiang Province from January 2004 to December 2016, this paper conducts an empirical analysis of early warning of provincial financial risks by TAR Vector Autoregressive Model and Ologit Probability Model. Results show that：(1) Inflation rate and export growth rate have significant and negative effects on provincial financial risks, while the new bank loans divided by industrial added value, fixed asset investment growth rate, consumption growth rate and import and export growth rate have significant and positive effects; (2) Financial stress risk of the previous month has a significant and negative effect on that of the current month, which makes the financial stress risk of the current month has some “smoothing” effect; (3) Statistical tests show that Dynamic Logit Early Warning Model is obviously better than Static Logit Early Warning Model in the model's overall significance, goodness of fit, and the accuracy of the prediction; （4） The early warning statistical analysis results of Zhejiang province in the recent 12 months show that strict interval of early warning accuracy is 25%, but the main class interval of early warning accuracy is 50%, and it is relatively better than early warning effect of domestic and foreign models of the same type.

Key words: Ologit model , provincial financial risk , model warning , leading indicator