Abstract
This paper offers new evidence on the dynamic behavior of
multifactor models. Specifically, we investigate the significance and temporal
stability of conditional factor betas in the context of multifactor asset
pricing models. Using a Kalman filter approach, we find that conditional factor
betas are dynamic and their statistical significance varies over time.
Furthermore, the inclusion of more factors improves that statistical
significance and time stability of the market factor. Overall, our empirical results support the
view that multifactors may not be independent risk factors but help to better identify
the market factor.
JEL classification numbers: G11; G12
Keywords: Asset Pricing, Risk Factors.