When betas meet the cross section: a hybrid risk model for equity portfolios

Benoit Vaucher and Matteo Bagnara

Journal of Risk

Abstract: We develop an innovative application of Kelly et al’s 2018 instrumented principal component analysis model, wherein regression-based exposures (betas) to risk factors are used as characteristics. We show that this new type of model, which hybridizes elements from cross-sectional, statistical and time series models, has many advantages. It inherits the high precision and depth of analysis typically found in cross-sectional models, while dramatically reducing their data requirements. In addition, it is precise, allows the inclusion of many characteristics while remaining numerically stable, and greatly simplifies the construction of multiregional models. Finally, although calibrated using a universe of funds, this model has excellent precision and low bias when used to analyze and optimize portfolios of stocks.

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