This article develops a new conditional jump model to study jump dynamics in Korean stock market index return. We propose a simple filter to infer the ex post distribution of jumps. This permits construction of the shock affecting the time t conditional jump intensity and is the main input into an autoregressive conditional jump intensity model. The model allows the conditional jump intensity to be time-varying and follows an approximate autoregressive moving average(ARMA) form. The time series characteristics of 11 years of four East-asian daily index returns are analysed using the jump model coupled with a generalized autoregressive conditional heteroskedasticity(GARCH) specification of volatility. We find significant time variation in the conditional jump intensity and evidence of time variation in the jump size distribution. Also, the most proper model is varied by sample data or period. And, the jump intensity and jump size are also different between countries. The jump shock is persistent, but the expected result of the switching in the jump direction did not found. This issue should be explored in future works.