Improving the Investment Process with a Custom Risk Model: A Case Study with the GLER Model

Abstract

The three ingredients in a mean–variance optimization model are the expected returns, the risk model, and constraints representing the portfolio manager’s mandates. Misalignment between the alpha vector and the risk model occurs when the alpha vector is not completely spanned by the factors in the risk model. It results in the optimizer taking large exposures on factors that have systematic risk but are missing from the risk model. With constraints, misalignment arises between the implied alpha and the risk model, resulting in portfolios that suffer from risk underestimation, undesired exposures to factors with hidden systematic risk, a consistent failure of the portfolio manager to achieve ex ante performance targets, and an intrinsic inability to transform superior alphas into outperforming portfolios. The authors use the global expected return (GLER) study to highlight the important role of custom risk models in addressing the misalignment between the implied alpha and the risk model. More specifically, they show that custom risk models 1) alleviate the risk-underestimation problem; 2) represent the alpha signal in the portfolio in an optimal risk-adjusted fashion, thereby delivering portfolios with high information ratios; and 3) generate a intuitive and useful ex post performance attribution analysis of the portfolio.