In this paper, we study a threshold-kernel estimation method for jump-diffusion processes, which iteratively applies thresholding and kernel methods in an approximately optimal way to achieve improved finite-sample performance. As in Figueroa-L\'opez and Nisen (2013), we use the expected number of jump misclassification as the objective function to optimally select the threshold parameter of the jump detection scheme. We prove that the objective function is quasi-convex and obtain a novel second-order infill approximation of the optimal threshold , hence extending results from the aforementioned paper . The approximate optimal threshold depends not only on the spot volatility \sigma_t , but also turns out to be a decreasing function of the jump intensity and the value of the jump density at the origin. The estimation methods for these quantities are then developed, where the spot volatility is estimated by a kernel estimator with a threshold and the value of the jump density at the origin is estimated by a density kernel estimator applied to those increments deemed to contains jumps by the chosen thresholding criterion. Due to the interdependency between the model parameters and the approximate optimal estimators designed to estimate them, a type of iterative fixed-point algorithm is developed to implement them. Simulation studies  show that it is not only feasible to implement the higher-order local optimal threshold scheme but also that this is superior to those based only on the first order approximation and/or on average values of the parameters over the estimation time period.
In this paper, we propose a new threshold-kernel jump-detection method for jump-diffusion processes, which iteratively applies thresholding and kernel methods in an approximately optimal way to achieve improved finite-sample performance. We use the expected number of jump misclassifications as the objective function to optimally select the threshold parameter of the jump detection scheme. We prove that the objective function is quasi-convex and obtain a new second-order infill approximation of the optimal threshold in closed form . The approximate optimal threshold depends not only on the spot volatility , but also the jump intensity and the value of the jump density at the origin. Estimation methods for these quantities are then developed, where the spot volatility is estimated by a kernel estimator with thresholding and the value of the jump density at the origin is estimated by a density kernel estimator applied to those increments deemed to contains jumps by the chosen thresholding criterion. Due to the interdependency between the model parameters and the approximate optimal estimators built to estimate them, a type of iterative fixed-point algorithm is developed to implement them. Simulation studies for a prototypical stochastic volatility model show that it is not only feasible to implement the higher-order local optimal threshold scheme but also that this is superior to those based only on the first order approximation and/or on average values of the parameters over the estimation time period.
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