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, Lamont, 2001). We also incorporate potential empirical estimation improvements through machine learning methodologies. We provide an application to the construction of tradable portfolios mimicking three global macro factors such as growth, inflation surprises, and financial stress indicators.

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

JEL Classification: G11, D81, C60