Shocks-adaptive Robust Minimum Variance Portfolio for a Large Universe of Assets

TL;DR

This paper introduces a shocks-adaptive robust minimum variance portfolio for large asset universes, utilizing a novel robust PCA and shrinkage covariance estimation to handle outliers and shocks effectively.

Contribution

It develops a robust factor model and portfolio optimization method that adaptively manages shocks without distinguishing between global and idiosyncratic ones.

Findings

Superior empirical performance demonstrated

Effective handling of heavy tails and shocks

Robust portfolio outperforms traditional methods

Abstract

This paper proposes a robust, shocks-adaptive portfolio in a large-dimensional assets universe where the number of assets could be comparable to or even larger than the sample size. It is well documented that portfolios based on optimizations are sensitive to outliers in return data. We deal with outliers by proposing a robust factor model, contributing methodologically through the development of a robust principal component analysis (PCA) for factor model estimation and a shrinkage estimation for the random error covariance matrix. This approach extends the well-regarded Principal Orthogonal Complement Thresholding (POET) method (Fan et al., 2013), enabling it to effectively handle heavy tails and sudden shocks in data. The novelty of the proposed robust method is its adaptiveness to both global and idiosyncratic shocks, without the need to distinguish them, which is useful in forming portfolio weights when facing outliers. We develop the theoretical results of the robust factor model and the robust minimum variance portfolio. Numerical and empirical results show the superior performance of the new portfolio.