Robust multivariate modeling in finance

Purpose – Proposes a new covariance matrix robust estimator able to capture the correct orientation of the data and the large unconditional variance caused by occasional high volatility periods. Design/methodology/approach – Derives easy‐to‐compute estimates for the center and covariance matrix of a data set. The method finds the correct orientation of the data through a robust estimator and the variances through the classical sample covariance matrix. Findings – Simulation experiments confirm the good performance of the proposed estimator under ε ‐contaminated normal models and multivariate t ‐distributions. Practical implications – Provides illustrations of the usefulness of this practical tool for the finance industry, in particular when constructing efficient frontiers. Shows that robust portfolios yield higher cumulative returns and possess more stable weights compositions. Originality/value – It provides an alternative estimator for the covariance matrix able to find a good fit for the bulk of the data as well as for the extreme observations, which could be plugged in widely used financial tools.