Conditional Jump Dynamics in Stock Market Returns

We develops a new conditional jump model to study jump dynamics in stock market returns. We propose a simple filter to infer ex post the 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 ARMA form. The time-series characteristics of 72 years of daily stock returns are analyzed using the jump model coupled with a GARCH specification of volatility. We find significant time-variation in the conditional jump intensity an evidence of time-variation in the jump size distribution. The conditional jump dynamics contribute to a good in-sample and out-of-sample fit to stock market volatility, and capture the rally often observed in equity markets following a significant downturn