Predictive information criterion for jump diffusion processes
By: Yuma Uehara
Potential Business Impact:
Finds best math models for fast-changing data.
In this paper, we address a model selection problem for ergodic jump diffusion processes based on high-frequency samples. We evaluate the expected genuine log-likelihood function and derive an Akaike-type information criterion based on the threshold-based quasi-likelihood function. In the derivation process, we also give new estimates of the transition density of jump diffusion processes. We also provide the relative selection probability of the proposed information criterion.
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