Robust inference of risks of large portfolios

Jianqing Fan, Fang Han, Han Liu, Byron Vickers

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)298-308
Number of pages11
JournalJournal of Econometrics
Issue number2
StatePublished - Oct 1 2016

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • Economics and Econometrics


  • High dimensionality
  • Quantile statistics
  • Rank statistics
  • Risk management
  • Robust inference


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