ESTIMATION OF STOCHASTIC VOLATILITY MODELS BY NONPARAMETRIC FILTERING
提出两步估计法：先非参数估计未观测的瞬时波动率过程，再用标准方法估计扩散模型，适用于参数和非参数模型，可处理跳跃和市场微观结构噪声。
A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonparametrically estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the filtered/estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties of the proposed estimators.