Affiliations: [a] School of Management, Wuhan University of Technology, Wuhan, China
| [b] Business School, Jianghan University, Wuhan, China
| [c] School of Economics and Management, Wuhan University, Wuhan, China
Abstract: With the rapid development of China’s economy, early-warning of operational risk has attracted significant attention of financial risk researchers in recent years. To analysis dynamic early-warning of operational risks, Chinese manufacturing industry publicly listed companies were selected as research object. A measure model of early-warning index of operational risks in Chinese manufacturing industry listed companies based on grey Kalman Filter was constructed. With the early-warning results of sample listed companies in manufacturing industry through calculating process noise covariance, measurement noise covariance and Kalman gain matrix; thirdly, a model of investors’ reactions was designed to test correctness of early-warning index of operational risks. Results demonstrate that, for the case study, it can get precise result of early-warning results by using Kalman filter, and investors react negatively towards the operational risks. The conclusions contribute to prior literatures on the dynamic evaluation of early-warning of operational risks by providing operational processes.
