Robust Inference of Risks of Large Portfolios
45 Pages Posted: 12 Jan 2015
Date Written: January 10, 2015
We propose a bootstrap-based robust high-confidence level upper bound (Robust H-CLUB) for assessing the risks of large portfolios. The proposed approach exploits rank-based and quantile-based estimators, and can be viewed as a robust extension of the H-CLUB method (Fan et al., 2015). Such an extension allows us to handle possibly misspecified models and heavy-tailed data. Under mixing conditions, we analyze the proposed approach and demonstrate its advantage over the H-CLUB. We further provide thorough numerical results to back up the developed theory. We also apply the proposed method to analyze a stock market dataset.
Keywords: High dimensionality; robust inference; rank statistics; quantile statistics; risk management; covariance matrix.
JEL Classification: G11, C14, G32, C58
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