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Conditional beta pricing models: A nonparametric approach

Eva Ferreira (), Javier Gil-Bazo and Susan Orbe

Journal of Banking & Finance, 2011, vol. 35, issue 12, 3362-3382

Abstract: We propose a two-stage procedure to estimate conditional beta pricing models that allows for flexibility in the dynamics of asset betas and market prices of risk (MPR). First, conditional betas are estimated nonparametrically for each asset and period using the time-series of previous data. Then, time-varying MPR are estimated from the cross-section of returns and betas. We prove the consistency and asymptotic normality of the estimators. We also perform Monte Carlo simulations for the conditional version of the three-factor model of Fama and French (1993) and show that nonparametrically estimated betas outperform rolling betas under different specifications of beta dynamics. Using return data on the 25 size and book-to-market sorted portfolios, we find that the nonparametric procedure produces a better fit of the three-factor model to the data, less biased estimates of MPR and lower pricing errors than the Fama–MacBeth procedure with betas estimated under several alternative parametric specifications.

Keywords: Kernel estimation; Conditional beta pricing models; Fama–French three-factor model; Locally stationary processes (search for similar items in EconPapers)
JEL-codes: G12 C14 C32 (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1016/j.jbankfin.2011.05.016

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