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Robust inference of risks of large portfolios

Author

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  • Fan, Jianqing
  • Han, Fang
  • Liu, Han
  • Vickers, Byron

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 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.

Suggested Citation

  • Fan, Jianqing & Han, Fang & Liu, Han & Vickers, Byron, 2016. "Robust inference of risks of large portfolios," Journal of Econometrics, Elsevier, vol. 194(2), pages 298-308.
    • Handle: RePEc:eee:econom:v:194:y:2016:i:2:p:298-308
    DOI: 10.1016/j.jeconom.2016.05.008
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    More about this item

    Keywords

    High dimensionality; Robust inference; Rank statistics; Quantile statistics; Risk management;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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