Double Smoothed Volatility Estimation of Potentially Non‐stationary Jump‐diffusion Model of Shibor
提出一种基于高频数据的双重平滑非参数方法，用于估计跳跃扩散模型的条件波动率，并证明了估计量的强一致性和渐近正态性，通过蒙特卡洛模拟和Shibor数据验证了有限样本性质。
The occurrence‐50 of economic policies and other sudden and large shocks often bring out jumps in financial data, which can be characterized through continuous‐time jump‐diffusion model. In this article, we present the double smoothed non‐parametric approach for infinitesimal conditional volatility of jump‐diffusion model based on high frequency data. Under certain minimal conditions, we obtain the strong consistency and asymptotic normality for the estimator as the time span T → ∞ and the sample interval . The procedure and asymptotic behavior can be applied for both Harris recurrent and positive Harris recurrent processes. The finite sample properties of the underlying double smoothed volatility estimator are verified through Monte Carlo simulation and Shanghai Interbank Offered Rate in China for application.