Macro Factor Mimicking Portfolios

Abstract

The estimation of risk factors and their replication through mimicking portfolios are of critical importance for academics and practitioners in finance. We propose a general optimization framework to construct macro factor mimicking portfolios that encompasses existing portfolio mimicking approaches, such as two-pass cross-sectional regression models (Fama and MacBeth, 1973) and maximal correlation approaches (Huberman et al., 1987, and Lamont, 2001). We incorporate empirical estimation improvements through machine learning methodologies. We provide an application to the construction of tradable portfolios mimicking three global macro factors, namely growth, inflation surprises, and financial stress indicators. We show how these macro mimicking factors can be used to improve the risk-return profile of a typical endowment multi-asset portfolio.

Keywords: factor investing, mimicking portfolios, portfolio optimization, macro risk management, machine learning

JEL Classification: G11, D81, C60