A rank test for the number of factors with high-frequency data

A rank test for the number of factors with high-frequency data
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高频数据因素数量的排名检验

DOI:
10.1016/j.jeconom.2019.03.004
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发表时间:
2019-08
影响因子:
6.3
通讯作者:
Wang Zhou
Wang Zhou
中科院分区:
经济学2区
文献类型:
--
作者:
Xin-Bing Kong;Zhi Liu;Wang Zhou

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在文献中,最近对大维因子模型的因子数量估计的一致性进行了广泛的研究。但由于估计量缺乏极限分布,估计量的二阶性质长期以来一直没有得到解决。在本文中,我们提出使用受微观结构噪声污染的大面板高频数据对因子数量进行排序检验。排名测试是通过形成固定数量的投资组合来实现的,将维度减少到有限数量。在构建投资组合的过程中,因子的数量渐近等于多元化投资组合波动性矩阵的秩。通过估计投资组合的低维价格动态的波动性等级,我们建立了估计因子数的中心极限定理。然后,我们将渐近正态性应用于对因子数量的检验。包括蒙特卡罗模拟和实际数据分析在内的数值实验证明了我们的理论。
In the literature, consistency of the estimates of the number of factors for large-dimensional factor models had been extensively studied recently. But the second-order property of the estimator has long been unsolved due to lack of limiting distribution of the estimators. In this paper, we propose a rank test of the number of factors using large panel high-frequency data contaminated with microstructure noise. The rank test is realized by forming a fixed number of portfolios which reduce the dimension to a finite number. In the process of constructing portfolios, the number of factors is equal to the rank of the volatility matrix of the diversified portfolios asymptotically. Via estimating the volatility rank of a low-dimensional price dynamics of the portfolios, we establish a central limit theorem of the estimated factor number. We then apply the asymptotic normality to testing on the number of factors. Numerical experiments including the Monte-Carlo simulations and real data analysis justify our theory.
DOI: 10.2139/ssrn.2669506
发表时间: 2016-10
期刊: Econometric Modeling: Capital Markets - Risk eJournal
影响因子: --
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