Robust Inference of Risks of Large Portfolios.
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 procedure (Fan et al., 2015). Such an extension allows us to handle possibly misspecified models and heavy-tailed data, which are stylized features in financial returns. Under mixing conditions, we analyze the proposed approach and demonstrate its advantage over H-CLUB. We further provide thorough numerical results to back up the developed theory, and also apply the proposed method to analyze a stock market dataset.
Department of Operations Research and Financial Engineering, Princeton University, USA.
Jianqing
J
Fan
Department of Statistics, University of Washington, WA 98195, USA.
Fang
F
Han
Department of Operations Research and Financial Engineering, Princeton University, USA.
Han
H
Liu
Department of Operations Research and Financial Engineering, Princeton University, USA.
Byron
B
Vickers
High dimensionality; quantile statistics; rank statistics; risk management; robust inference