Robust inference of risks of large portfolios

Jianqing Fan, Fang Han, Han Liu and Byron Vickers

Journal of Econometrics, 2016, vol. 194, issue 2, 298-308

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.

Keywords: High dimensionality; Robust inference; Rank statistics; Quantile statistics; Risk management (search for similar items in EconPapers)
JEL-codes: C58 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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Working Paper: Robust Inference of Risks of Large Portfolios (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:194:y:2016:i:2:p:298-308

DOI: 10.1016/j.jeconom.2016.05.008

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