We propose a new model called RACB (Realised Autoregressive Conditional Beta) for the dynamics of slope coefficients, or betas, in linear regression models with heteroskedastic errors. RACB is a quasi score-driven model that jointly models returns and realised betas, conditional on the regressors.
- Speaker
- Date
- Thursday 26 Nov 2026, 12:00 - 13:00
- Type
- Seminar
- Room
- ET-14
- Location
- Campus Woudestein
Written with Mariia Artemova and Christian Francq
It extends the Autoregressive Conditional Beta (ACB) model by incorporating realised betas into the beta updating equation and shrinking the conditional betas toward the realised betas. We develop a multistep quasi-maximum likelihood estimator for the RACB model and establish its large-sample properties.
We also derive key stochastic properties of the data-generating process and the associated filter, and show that the contraction condition ensuring filter invertibility is not only sufficient but also almost necessary. When realised betas are missing, we show that replacing the unobserved realised betas with their conditional counterparts is optimal within a class of near-degenerate approximations and for the one-step tracking loss.
Empirically, for 90 large U.S. stocks in a Fama-French three-factor setting, the RACB model achieves the best performance in a tracking portfolio exercise as it is the most frequently retained model in the Model Confidence Set.
See also
- More information
Do you want to know more about the event? Contact the secretariat Econometrics at eb-secr@ese.eur.nl.
