Optimal Candlestick-Based Spot Volatility Estimation: New Tricks and Feasible Inference Procedures
推导了基于K线的瞬时波动率估计的新积分表示，使多K线最优估计可计算，并提出了精确抽样方案，展示了在新闻公告前后市场波动行为中的应用价值。
Abstract We contribute to the growing literature on high-frequency spot volatility estimation by deriving a new integral representation for the recently introduced asymptotic minimum risk equivariant (AMRE) candlestick-based class of estimators. Our new theoretical representation enables the practical numerical computation of the hitherto impractical to compute optimal estimators based on multiple adjacent candlesticks. We also propose a new exact sampling scheme for high-frequency candlestick data, which facilitates straightforward calculation of the asymptotic risk and confidence intervals for the estimators. The resulting critical values for the highest-density intervals highlight the substantial efficiency gains from incorporating more than one candlestick in the estimation process. We showcase the practical value of the new techniques in elucidating the behavior of financial market volatility around the time of important news announcements.