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Standard empirical investigations of jump dynamics in returns and volatility are fairly complicated due to the presence of latent continuous-time factors. The authors present a new discrete-time framework that combines heteroskedastic processes with rich specifications of jumps in returns and volatility. The models can be estimated with ease using standard maximum likelihood techniques. They provide a tractable risk neutralization framework for this class of models which allows for separate modeling of risk premia for the jump and normal innovations. They anchor the models in the literature by providing continuous time limits of the models.
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