Preaveraging-Based Estimation of Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence
发展了资产价格日二次变分的预平均估计量理论，考虑了自相关微观结构噪声和跳跃，提出了序列相关检验，并基于纽交所交易数据提供了最优实现指南。
This article contributes to the theory for preaveraging estimators of the daily quadratic variation of asset prices and provides novel empirical evidence. We develop asymptotic theory for preaveraging estimators in the case of autocorrelated microstructure noise and propose an explicit test for serial dependence. Moreover, we extend the theory on preaveraging estimators for processes involving jumps. We discuss several jump-robust measures and derive feasible central limit theorems for the general quadratic variation. Using transaction data of different stocks traded at the New York Stock Exchange, we analyze the estimators' sensitivity to the choice of the preaveraging bandwidth. Moreover, we investigate the dependence of preaveraging-based inference on the sampling scheme, the sampling frequency, microstructure noise properties, and the occurrence of jumps. As a result of a thorough empirical study, we provide guidance for optimal implementation of preaveraging estimators and discuss potential pitfalls in practice.