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open-access-imgOpen AccessGeneralized Autoregressive Conditional Betas: A New Multivariate Score-Driven Filter
Author(s)
Blazsek Szabolcs,
Jörding August,
Rai Simran
Publication year2024
Publication title
studies in nonlinear dynamics and econometrics
Resource typeJournals
PublisherDe Gruyter
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.
Keyword(s)systematic risk, dynamic conditional score (DCS), generalized autoregressive score (GAS), autoregressive conditional beta (ACB), score-driven volatility, maximum likelihood (ML) estimation
Language(s)English
SCImago Journal Rank0.453
H-Index31
eISSN1558-3708
pISSN1081-1826
DOI10.1515/snde-2023-0019

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