This paper addresses the dynamic tail risk in price-limited financial markets. We propose a novel censored autoregressive conditional Fr´echet model with a fiexible evolution scheme for the time-varying parameters, which allows deciphering the impact of historical information on tail risk from the viewpoint of different risk preferences. The proposed model can well accommodate many important empirical characteristics, such as thick-tailness, extreme risk clustering, and price limits. The empirical analysis of the Chinese stock market reveals the effectiveness of our model in interpreting and predicting time-varying tail behaviors in price-limited equity markets, providing a new tool for financial risk management.