Systemic Risk Monitoring Model from the Perspective of Public Information Arrival

Based on the perspective of public information arrival, this paper establishes a theoretical framework to indirectly estimate systemic anomalies by measuring information arrival. We propose a novel method, the market correlation deviation test, for identifying systemic anomalies and implementing market monitoring using high-frequency price data from the Chinese stock market. This method achieves a reduction in time complexity from T(N2) to T (N) compared to traditional covariance monitoring models, without sacrificing measurement accuracy. By comparing with conventional Barndorff-Nielson-Shephard (BNS) and Corsi-Pirino-Reno (CPR) models, our approach demonstrates superior identification accuracy and monitoring efficiency. Particularly, it excels in detecting widespread small synchronous movements in stock prices (Type A anomalies). Furthermore, we analyze the distribution of market prices and trading volumes after the arrival of public information and observe significant information loadings associated with public information arrival.Key words: System Risk; Public Information Arrival; Risk Monitoring Model; Covariance Monitoring Model; Index Monitoring Model; Individual Stock Monitoring ModelJEL: C58 G11 C14