ON THE SYSTEMATIC AND IDIOSYNCRATIC VOLATILITY WITH LARGE PANEL HIGH-FREQUENCY DATA

• 成果类型: Article
• 署名作者: Kong, Xin-Bing
• 署名单位: Nanjing Audit University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/17-AOS1578
• 发表日期: 2018
• 页码: 1077-1108
• 关键词: dimensional covariance matrices; pure-jump-processes; INTEGRATED VOLATILITY; efficient estimation; realized volatility; financial data; models; SEMIMARTINGALES; arbitrage

摘要

In this paper, we separate the integrated (spot) volatility of an individual Ito process into integrated (spot) systematic and idiosyncratic volatilities, and estimate them by aggregation of local factor analysis (localization) with large-dimensional high-frequency data. We show that, when both the sampling frequency n and the dimensionality p go to infinity and p >= C root n for some constant C, our estimators of High dimensional Ito process; common driving process; specific driving process, integrated High dimensional Ito process, common driving process, specific driving process, systematic and idiosyncratic volatilities are root n (n(1/4) for spot estimates) consistent, the best rate achieved in estimating the integrated (spot) volatility which is readily identified even with univariate high-frequency data. However, when Cn(1/4) <= p < C root n, aggregation of n(1/4)-consistent local estimates of systematic and idiosyncratic volatilities results in p-consistent (not root n-consistent) estimates of integrated systematic and idiosyncratic volatilities. Even more interesting, when p < Cn(1/4), the integrated estimate has the same convergence rate as the spot estimate, both being p-consistent. This reveals a distinctive feature from aggregating local estimates in the low-dimensional highfrequency data setting. We also present estimators of the integrated (spot) idiosyncratic volatility matrices as well as their inverse matrices under some sparsity assumption. We finally present a factor-based estimator of the inverse of the spot volatility matrix. Numerical studies including the Monte Carlo experiments and real data analysis justify the performance of our estimators.