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Autoregressive conditional betas

Author

Listed:
  • F. Blasques

    (VU - Vrije Universiteit Amsterdam [Amsterdam])

  • Christian Francq

    (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique, IP Paris - Institut Polytechnique de Paris)

  • Sébastien Laurent

    (AMU - Aix Marseille Université)

Abstract

This paper introduces an autoregressive conditional beta (ACB) model that allows regressions with dynamic betas (or slope coefficients) and residuals with GARCH conditional volatility. The model fits in the (quasi) score-driven approach recently proposed in the literature, and it is semi-parametric in the sense that the distributions of the innovations are not necessarily specified. The time-varying betas are allowed to depend on past shocks and exogenous variables. We establish the existence of a stationary solution for the ACB model, the invertibility of the score-driven filter for the time-varying betas, and the asymptotic properties of one-step and multistep QMLEs for the new ACB model. The finite sample properties of these estimators are studied by means of an extensive Monte Carlo study. Finally, we also propose a strategy to test for the constancy of the conditional betas. In a financial application, we find evidence for time-varying conditional betas and highlight the empirical relevance of the ACB model in a portfolio and risk management empirical exercise.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • F. Blasques & Christian Francq & Sébastien Laurent, 2024. "Autoregressive conditional betas," Post-Print hal-05417169, HAL.
    • Handle: RePEc:hal:journl:hal-05417169
    DOI: 10.1016/j.jeconom.2023.105630
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    Cited by:

    1. Artemova, Mariia, 2025. "An order-invariant score-driven dynamic factor model," Journal of Econometrics, Elsevier, vol. 251(C).
    2. Alexander Mayer & Davide Raggi, 2025. "Estimation and inference in models with multiple behavioural equilibria," Papers 2512.04541, arXiv.org, revised Mar 2026.

    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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