Article ID Journal Published Year Pages File Type
1155973 Stochastic Processes and their Applications 2011 28 Pages PDF
Abstract

The problem of estimating the Lévy density of a partially observed multidimensional affine process from low-frequency and mixed-frequency data is considered. The estimation methodology is based on the log-affine representation of the conditional characteristic function of an affine process and local linear smoothing in time. We derive almost sure uniform rates of convergence for the estimated Lévy density both in mixed-frequency and low-frequency setups and prove that these rates are optimal in the minimax sense. Finally, the performance of the estimation algorithms is illustrated in the case of the Bates stochastic volatility model.

Related Topics
Physical Sciences and Engineering Mathematics Mathematics (General)
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