Robust Inference of Risks of Large Portfolios

Jianqing Fan, Fang Han, Han Liu and Byron Vickers

Papers from arXiv.org

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.

Date: 2015-01
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Citations: View citations in EconPapers (29)

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Related works:
Journal Article: Robust inference of risks of large portfolios (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1501.02382

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