Range‐Based Estimation of Stochastic Volatility Models
提出用价格区间数据来估计随机波动模型，理论、数值和实证都显示区间波动代理不仅高效，还近似正态且抗微观结构噪声，并用该方法发现日汇率波动呈现双因子结构。
ABSTRACT We propose using the price range in the estimation of stochastic volatility models. We show theoretically, numerically, and empirically that range‐based volatility proxies are not only highly efficient, but also approximately Gaussian and robust to microstructure noise. Hence range‐based Gaussian quasi‐maximum likelihood estimation produces highly efficient estimates of stochastic volatility models and extractions of latent volatility. We use our method to examine the dynamics of daily exchange rate volatility and find the evidence points strongly toward two‐factor models with one highly persistent factor and one quickly mean‐reverting factor.