Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach
Journal
European Financial Management
ISSN
1354-7798
ISSN-Digital
1468-036X
Type
journal article
Date Issued
2008-06-01
Author(s)
Abstract
We use Markov Chain Monte Carlo (MCMC) methods for the parameter estimation and testing of conditional asset pricing models. In contrast to traditional approaches, it is truly conditional because the assumption that time variation in betas is driven by a set of conditioning variables is not necessary. Moreover, the approach has exact finite sample properties and accounts for errors-in-variables. Using S&P 500 panel data, we analyze the empirical performance of the CAPM and the Fama and French (1993) three-factor model. We find that time-variation of betas in the CAPM and the time variation of the coefficients for the size factor (SMB) and the distress factor (HML) in the three-factor model improve the empirical performance by a similar amount. Therefore, our findings are consistent with time variation of firm-specific exposure to market risk, systematic credit risk and systematic size effects. However, a Bayesian model comparison trading off goodness of fit and model complexity indicates that the conditional CAPM performs best, followed by the conditional three-factor model, the unconditional CAPM, and the unconditional three-factor model.
http://www.manuel-ammann.com/pdf/PubsAmmann2007_MCMC_EFM.pdf
http://www.manuel-ammann.com/pdf/PubsAmmann2007_MCMC_EFM.pdf
Language
English
HSG Classification
not classified
Refereed
Yes
Publisher
Blackwell
Publisher place
Oxford
Volume
14
Number
3
Start page
391
End page
418
Pages
28
Subject(s)
Division(s)
Eprints ID
29074
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Name
WP21 Conditional Asset Pricing.pdf
Size
1.1 MB
Format
Adobe PDF
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