Robust inference of risks of large portfolios

Jianqing Fan Fang Han Han Liu Byron Vickers

Statistics Theory and Methods mathscidoc:1912.43425

Journal of econometrics, 194, (2), 298-308, 2016.10
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 etal., 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.
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  title={Robust inference of risks of large portfolios},
  author={Jianqing Fan, Fang Han, Han Liu, and Byron Vickers},
  booktitle={Journal of econometrics},
Jianqing Fan, Fang Han, Han Liu, and Byron Vickers. Robust inference of risks of large portfolios. 2016. Vol. 194. In Journal of econometrics. pp.298-308.
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