Abstract: We propose a new discrete-time model of returns in which jumps capture persistence in the conditional variance and higher-order moments. Jump arrival is governed by a heterogeneous Poisson process. The intensity is directed by a latent stochastic autoregressive process, while the jump-size distribution allows for conditional heteroskedasticity. Model evaluation focuses on the dynamics of the conditional distribution of returns using density and variance forecasts. Predictive likelihoods provide a period-by-period comparison of the performance of our heterogeneous jump model relative to conventional
SV and GARCH models. Further, in contrast to previous studies on the importance of jumps, we utilize realized volatility to assess out-of-sample variance forecasts.
Keywords: jump clustering, jump dynamics, MCMC, predictive likelihood, realized volatility, Bayesian model average
JEL Classification: C22, C11, G1