Incorporating Global Industrial Classification Standard Into Portfolio Allocation: A Simple Factor-Based Large Covariance Matrix Estimator With High-Frequency Data

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Subfields: Finance

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Understanding Systematic Risk: A High‐Frequency Approach (2020, unknown): “Alternative economic interpretations as suggested for example by French et al. (1987) and Campbell and Hentschel (1992) use a risk-premium argument.”
Principal Component Analysis of High-Frequency Data (2016, unknown): “…be employed to estimate parametric or semiparametric continuous-time models with a factor structure allowing for an increasing dimensionality: see Fan, Furger, and Xiu (2016) and Aït-Sahalia and Xiu (2017) for using factor analysis for covariance estimation and Pelger (2015a) and Pelger (2015b)…”
On the number of common factors with high‐frequency data (2017, unknown): “Similar empirical findings were reported in Fan et al. (2016).”
A Nodewise Regression Approach to Estimating Large Portfolios (2016, unknown): “Fan, Furger, and Xiu (2016) and Ait-Sahalia and Xiu (2017) investigated the accuracy of precision matrices estimated by using factor models, but do not analyze portfolio variances and weights when p > n. Fan, Fan, and Lv (2008) proposed a covariance matrix estimator assuming observable factors and…”
A Projection-based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models. (2015, result): “This is consistent with the findings that the within-sector correlations are still present even after adjusting for Fama–French factors and 10 industrial factors (Fan et al., 2016).”