We propose model-free (nonparametric) estimators of the volatility of volatility and leverage effect using high-frequency observations of short-dated options. At each point in time, we integrate available options into estimates of the conditional characteristic function of the price increment until the options' expiration and we use these estimates to recover spot volatility. Our volatility of volatility estimator is then formed from the sample variance and first-order autocovariance of the spot volatility increments, with the latter correcting for the bias in the former due to option observation errors. The leverage effect estimator is the sample covariance between price increments and the estimated volatility increments. The rate of convergence of the estimators depends on the diffusive innovations in the latent volatility process as well as on the observation error in the options with strikes in the vicinity of the current spot price. Feasible inference is developed in a way that does not require prior knowledge of the source of estimation error that is asymptotically dominating.

翻译：我们提出一种无模型（非参数）估计量，利用短期限期权的高频观测数据估计波动率的波动性与杠杆效应。在每个时间点，我们将可获取的期权信息整合为到期前价格增量的条件特征函数估计值，并利用这些估计值恢复瞬时波动率。随后，通过瞬时波动率增量的样本方差与一阶自协方差构建波动率波动性估计量，其中自协方差用于修正期权观测误差导致的样本方差偏差。杠杆效应估计量则通过价格增量与估计的波动率增量之间的样本协方差计算。这些估计量的收敛速度取决于潜在波动率过程的扩散性新息以及当前现货价格附近执行价期权的观测误差。我们开发了无需预先知晓渐近主导估计误差来源的可行推断方法。