英文文献：Estimation of Stochastic Volatility Models by Nonparametric Filtering-随机波动率模型的非参数滤波估计
英文文献作者：Shin Kanaya,Dennis Kristensen
英文文献摘要：
A two-step estimation method of stochastic volatility models is proposed: In the first step, we estimate the (unobserved) instantaneous volatility process using the estimator of Kristensen (2010, Econometric Theory 26). In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the filtered volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and we give theoretical results for both. The resulting estimators of the drift and diffusion terms of the volatility model will carry additional biases and variances due to the first-step estimation, but under regularity conditions 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.

提出了随机波动率模型的两步估计方法:第一步，利用Kristensen(2010，计量经济学理论26)的估计器估计(未观察到的)瞬时波动过程。第二步采用了完全观测扩散过程的标准估计方法，但用过滤后的波动过程代替了潜在过程。我们的估计策略同时适用于参数和非参数随机波动模型，并给出了两者的理论结果。波动率模型的漂移和扩散项的估计量会由于第一步估计而带有额外的偏差和方差，但在正则条件下，这些估计量渐近消失，我们的估计量继承了基于波动过程观察的不可行的估计量的渐近性质。一个模拟研究检验了所提估计量的有限样本性质。