Generalized Autoregressive Conditional Betas: A New Multivariate Score-Driven Filter

Szabolcs Blazsek, Jörding August () and Rai Simran ()
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Jörding August: 138563 Stetson-Hatcher School of Business, Mercer University , 1511-1565 College Street, Macon, GA 31207, USA
Rai Simran: 138563 Stetson-Hatcher School of Business, Mercer University , 1511-1565 College Street, Macon, GA 31207, USA

Studies in Nonlinear Dynamics & Econometrics, 2025, vol. 29, issue 1, 95-128

Abstract: In this paper, we extend the recent Gaussian autoregressive conditional beta (Gaussian-ACB) model from the literature on score-driven models. In the new asset pricing model, named the t generalized ACB (t-GACB) model, a multivariate score-driven filter for the t-distribution drives dynamic interaction effects among the beta coefficients. We present the econometric formulation and statistical inference for the t-GACB model, which we apply to 15 stocks from the United States (US) from 1999 to 2022. In our empirical application, we use the three Fama–French factors as asset pricing factors, and we also use the Volatility Index, TED Spread, and ICE BofA US High Yield Index Option-Adjusted Spread as exogenous explanatory variables that influence the beta coefficients. We compare the statistical and realized tracking error performances of the Gaussian-ACB, t-ACB, and t-GACB models, and we find that the t-GACB model improves the Gaussian-ACB model.

Keywords: systematic risk; dynamic conditional score (DCS); generalized autoregressive score (GAS); autoregressive conditional beta (ACB); score-driven volatility; maximum likelihood (ML) estimation (search for similar items in EconPapers)
JEL-codes: C32 C52 C58 G12 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1515/snde-2023-0019

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