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Poster
Robust Portfolio Optimization
Huitong Qiu · Fang Han · Han Liu · Brian Caffo

Mon Dec 07 04:00 PM -- 08:59 PM (PST) @ 210 C #82
in Poster Session »

We propose a robust portfolio optimization approach based on quantile statistics. The proposed method is robust to extreme events in asset returns, and accommodates large portfolios under limited historical data. Specifically, we show that the risk of the estimated portfolio converges to the oracle optimal risk with parametric rate under weakly dependent asset returns. The theory does not rely on higher order moment assumptions, thus allowing for heavy-tailed asset returns. Moreover, the rate of convergence quantifies that the size of the portfolio under management is allowed to scale exponentially with the sample size of the historical data. The empirical effectiveness of the proposed method is demonstrated under both synthetic and real stock data. Our work extends existing ones by achieving robustness in high dimensions, and by allowing serial dependence.

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Author Information

Huitong Qiu (Johns Hopkins University)
Fang Han (Johns Hopkins University)
Han Liu (Princeton University)
Brian Caffo

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