We develop a new model named the Common Auto-Regressive Jump Inten-sity Score-driven with Triggering probability (CARJIST) model. It captures the unobservable systemic risk and the ripple effect by applying the General-ized Autoregressive Score (GAS) framework. The unobservable systemic risk is modeled as a common jump with the time-varying intensity. The ripple effect is modeled as the time-varying probability of triggering the realization of common jumps. The model allows us to decompose the realization of sys-temic risk in multiple stock returns into the systemic risk and ripple effect components. We apply the model for the 2023 U.S. regional banking cri-sis. Our main findings are as follows: First, the systemic risk of the regional banks interacts with the large commercial banks, but the ripple effect does not. Second, we find that the Federal Funds rate and term spread impact the systemic risk and ripple effect of both regional banks and large commercial banks. Our estimate indicates the 1% increase in the Federal Funds rate leads to around 3.5% increase in the probability of common jumps among those re-gional banks. Third, we document that the systemic risk of the regional banks is affected by the aggregate deposit outflow whereas the ripple effect is influ-enced by the option-implied risk appetite of the stock investors. Finally, we find that the CoVaR-based linkage of the regional banks strengthened after the SVB collapse.