Nonparametric Dynamic Conditional Beta

John Maheu and Azam Shamsi

MPRA Paper from University Library of Munich, Germany

Abstract: This paper derives a dynamic conditional beta representation using a Bayesian semiparametric multivariate GARCH model. The conditional joint distribution of excess stock returns and market excess returns are modeled as a countably infinite mixture of normals. This allows for deviations from the elliptic family of distributions. Empirically we find the time-varying beta of a stock nonlinearly depends on the contemporaneous value of excess market returns. In highly volatile markets, beta is almost constant, while in stable markets, the beta coefficient can depend asymmetrically on the market excess return. The model is extended to allow nonlinear dependence in Fama-French factors.

Keywords: GARCH; Dirichlet process mixture; slice sampling (search for similar items in EconPapers)
JEL-codes: C32 C58 G10 G17 (search for similar items in EconPapers)
Date: 2016-09-16
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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https://mpra.ub.uni-muenchen.de/77424/8/MPRA_paper_77424.pdf revised version (application/pdf)

Related works:
Journal Article: Nonparametric Dynamic Conditional Beta* (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:73764

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