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This paper addresses the debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. We construct global minimum variance portfolios based on the constituents of the S&P 500. HF-based covariance matrix predictions are obtained by applying a blocked realized kernel estimator, different smoothing windows, various regularization methods and two forecasting models. We show that HF-based predictions yield a significantly lower portfolio volatility…

高维已实现协方差估计的分块与正则化方法AI 解读

SUMMARY We introduce a blocking and regularization approach to estimate high‐dimensional covariances using high‐frequency data. Assets are first grouped according to liquidity. Using the multivariate realized kernel estimator of Barndorff‐Nielsen et al. (2010), the covariance matrix is estimated block‐wise and then regularized. The performance of the resulting blocking and regularization (‘RnB’) estimator is analyzed in an extensive simulation study mimicking the liquidity…