Realized Mixed-Frequency Factor Models for Vast Dimensional Covariance Estimation
Realized Mixed-Frequency Factor Models for Vast Dimensional Covariance Estimation
复制标题
实现了大维协方差估计的混合频率因子模型
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Dick J. C. van Dijk
中科院分区：
文献类型：
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作者：
Karim Bannouh;M. Martens;R. Oomen;Dick J. C. van Dijk
本文引入了一个混合频率因子模型（MFFM）来估计资产收益的高维协方差矩阵。MFFM使用高频（日内）数据来估计因子（协）方差，使用特异质风险和低频（日）数据来估计因子负荷。我们建议使用高流动性资产，如交易所交易基金（ETF）作为因素。这些合约的价格基本上没有高频的微观结构噪音，使我们能够获得因子协方差的精确估计。相反，因子载荷是从每日数据中估计的，以避免由于市场微观结构效应而产生的偏差，例如个股的相对非流动性以及因子和股票回报之间的不同步性。我们的理论、模拟和实证结果表明，与仅基于低频数据的传统因子模型和基于高频数据的流行实现协方差估计器相比，MFFM的性能非常出色。
We introduce a Mixed-Frequency Factor Model (MFFM) to estimate vast dimensional covari- ance matrices of asset returns. The MFFM uses high-frequency (intraday) data to estimate factor (co)variances and idiosyncratic risk and low-frequency (daily) data to estimate the factor loadings. We propose the use of highly liquid assets such as exchange traded funds (ETFs) as factors. Prices for these contracts are observed essentially free of microstructure noise at high frequencies, allowing us to obtain precise estimates of the factor covariances. The factor loadings instead are estimated from daily data to avoid biases due to market microstructure effects such as the relative illiquidity of individual stocks and non-synchronicity between the returns on factors and stocks. Our theoretical, simulation and empirical results illustrate that the performance of the MFFM is excellent, both compared to conventional factor models based solely on low-frequency data and to popular realized covariance estimators based on high-frequency data.