Robust Inference of Risks of Large Portfolios
Statistics Theory
2015-01-13 v1 Portfolio Management
Statistics Theory
Abstract
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
Cite
@article{arxiv.1501.02382,
title = {Robust Inference of Risks of Large Portfolios},
author = {Jianqing Fan and Fang Han and Han Liu and Byron Vickers},
journal= {arXiv preprint arXiv:1501.02382},
year = {2015}
}
Comments
45 pages, 2 figures