High-frequency volatility of volatility estimation free from spot volatility estimates
提出一种仅基于波动率过程傅里叶系数预估计的积分波动率波动率一致估计量，在噪声污染下偏差随观测数增加而消失，模拟和实证表明该估计量易于实现、计算稳定且对市场微观结构噪声稳健。
We define a new consistent estimator of the integrated volatility of volatility based only on a pre-estimation of the Fourier coefficients of the volatility process. We investigate the finite sample properties of the estimator in the presence of noise contaminations by computing the bias of the estimator due to noise and showing that it vanishes as the number of observations increases, under suitable assumptions. In both simulated and empirical studies, the performance of the Fourier estimator with high-frequency data is investigated and it is shown that the proposed estimator of volatility of volatility is easily implementable, computationally stable and even robust to market microstructure noise.