Adaptive thresholding for large volatility matrix estimation based on high-frequency financial data
Adaptive thresholding for large volatility matrix estimation based on high-frequency financial data
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DOI：
10.1016/j.jeconom.2017.09.006
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发表时间：
2018-03-01
影响因子：
6.3
通讯作者：
Wang, Yazhen
中科院分区：
文献类型：
--
作者：
Kim, Donggyu;Kong, Xin-Bing;Wang, Yazhen
基于高频金融数据的大规模稀疏综合波动率矩阵的估计，已经发展出了通用的阈值方法。由于综合波动率矩阵中的元素往往具有较大的变异性，因此通用阈值估计方法没有考虑到元素的变化，可能会有不令人满意的性能。本文研究了大型综合波动率矩阵的自适应阈值估计问题。我们首先构建了一个估计的渐近方差的预平均已实现波动率估计，然后使用这两个估计开发一个自适应阈值估计的大波动率矩阵。结果表明，当资产数量和样本容量都趋于无穷大时，自适应阈值估计在稀疏综合波动率矩阵类上可以达到最优收敛速度，而通用阈值估计只能达到次优收敛速度.此外，我们还讨论了如何利用自适应阈值方案的近似因子模型。仿真研究进行了检查的自适应阈值估计的有限样本性能。(C)2017爱思唯尔B.V.保留所有权利。
Universal thresholding methods have been developed to estimate the large sparse integrated volatility matrix of underlying assets based on high-frequency financial data. Since the integrated volatility matrix often has entries with a wide range of variability, universal thresholding estimators do not take the varying entries into consideration and may have unsatisfactory performances. This paper investigates adaptive thresholding estimation of the large integrated volatility matrix. We first construct an estimator for the asymptotic variance of the pre-averaging realized volatility estimator and then use the two estimators to develop an adaptive thresholding estimator of the large volatility matrix. It is shown that the adaptive thresholding estimator can achieve the optimal rate of convergence over the class of the sparse integrated volatility matrix when both the number of assets and sample size are allowed to go to infinity, while the universal thresholding estimator can achieve only the sub-optimal convergence rate. Also we discuss how to harness the adaptive thresholding scheme in the approximate factor model. The simulation study is conducted to check the finite sample performance of the adaptive thresholding estimators. (C) 2017 Elsevier B.V. All rights reserved.