Systemic risk monitoring model from the perspective of public information arrival

Abstract

We introduce a new technique called the market correlation deviation test, which aims to detect systemic anomalies and facilitate market monitoring by using high-frequency price data from the Chinese stock market. Our method achieves a significant reduction in calculation time (to one N of the traditional method, where N represents the number of stocks) while maintaining the accuracy of calculations. This reduction is achieved by converting the covariance calculation into a difference calculation. By comparing our approach with the conventional Barndorff-Nielson-Shephard (BNS) and Corsi-Pirino-Reno (CPR) models, we demonstrate superior accuracy in identifying anomalies and efficiency in monitoring. Notably, our method can measure local price synchronization behavior, often associated with the arrival of public information, and we also observe the impact of public information on pricing. To the best of our knowledge, this study is the first to directly observe and quantify the effect of public information on pricing.