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Robust Inference of Risks of Large Portfolios

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

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.

Suggested Citation

  • Jianqing Fan & Fang Han & Han Liu & Byron Vickers, 2015. "Robust Inference of Risks of Large Portfolios," Papers 1501.02382, arXiv.org.
    • Handle: RePEc:arx:papers:1501.02382
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    JEL classification:

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

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