Efficient Multipowers*
研究如何通过最优幂次的线性组合最小化多幂次估计量的渐近方差，构建更优的积分波动率估计量，并基于模拟和美股数据验证了高效四次幂估计量能大幅降低均方误差、减少跳跃误检并提升波动率预测质量。
We show how to minimize the asymptotic variance of multipower estimators using a linear combination of optimal powers. Taking advantage of the lower variance provided by this technique allows to build superior estimators of integrated volatility powers. In particular, we focus on a new efficient quarticity estimator and we show, using simulated data, that we can drastically reduce the mean square error of traditional estimators. The implementation on US stock prices corroborates our theoretical findings and further shows that ecient quarticity noticeably reduces the number of detected jumps, and improves the quality of volatility forecasts.