Journal of Risk
ISSN:
1755-2842 (online)
Editor-in-chief: Farid AitSahlia
When betas meet the cross section: a hybrid risk model for equity portfolios
Need to know
- We developed a hybrid risk model with elements of cross-sectional and time-series models.
- This model exhibit high-precision with much reduced data requirements.
- The proposed model works for funds as well as equities, for both analysis and optimisation.
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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