Portfolio diversification and model uncertainty: a robust dynamic mean-variance approach
Huyen Pham (LPSM (UMR\_8001), ENSAE), Xiaoli Wei (LPSM (UMR\_8001)),, Chao Zhou (NUS)

TL;DR
This paper develops a continuous-time robust dynamic mean-variance portfolio model under model uncertainty, analyzing how ambiguity about returns and correlations affects diversification and providing explicit quantifications of under-diversification.
Contribution
It introduces a separation principle for robust control under time-varying ambiguity, linking optimal strategies to minimal risk premiums and quantifying under-diversification effects.
Findings
Investor with high ambiguity on returns holds no risky assets.
Large ambiguity on correlation leads to holding only one risky asset.
Explicit bounds relate correlation uncertainty to diversification levels.
Abstract
This paper focuses on a dynamic multi-asset mean-variance portfolio selection problem under model uncertainty. We develop a continuous time framework for taking into account ambiguity aversion about both expected return rates and correlation matrix of the assets, and for studying the join effects on portfolio diversification. The dynamic setting allows us to consider time varying ambiguity sets, which include the cases where the drift and correlation are estimated on a rolling window of historical data or when the investor takes into account learning on the ambiguity. In this context, we prove a general separation principle for the associated robust control problem, which allows us to reduce the determination of the optimal dynamic strategy to the parametric computation of the minimal risk premium function. Our results provide a justification for under-diversification, as documented in…
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