Asymptotic Inference for Jump Diffusions with State‐Dependent Intensity
研究了高频采样下状态依赖强度跳跃扩散过程的局部渐近正态性，证明漂移和跳跃参数的推断可自适应于可一致估计的波动率参数。
Abstract We establish the local asymptotic normality property for a class of ergodic parametric jump‐diffusion processes with state‐dependent intensity and known volatility function sampled at high frequency. We prove that the inference problem about the drift and jump parameters is adaptive with respect to parameters in the volatility function that can be consistently estimated.