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Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach
Author(s) -
Ammann Manuel,
Verhofen Michael
Publication year - 2008
Publication title -
european financial management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.311
H-Index - 64
eISSN - 1468-036X
pISSN - 1354-7798
DOI - 10.1111/j.1468-036x.2007.00359.x
Subject(s) - capital asset pricing model , econometrics , markov chain monte carlo , monte carlo method , factor analysis , goodness of fit , mathematics , statistics , economics
We use Markov Chain Monte Carlo (MCMC) methods for the parameter estimation and the 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 analyse the empirical performance of the CAPM and theFama 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. 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.