Which Factors are Risk Factors in Asset Pricing? A Model Scan Framework

Comparing Asset Pricing Factor Models under Multivariate t-Distribution: Evidence from China

“…Using the same method, we further test the candidate models' ability to price a large set of return anomalies and find that our four-factor model also outperforms the other models. Lastly, considering that the foregoing results are based on frequentist approaches, we complement the empirical analysis using a newly developed Bayesian methodology under t-distributions [14]. e results confirm that our four-factor model dominates.…”

“…Higher marginal likelihoods indicate better performances in fitting real data. We adopt a recent Bayesian method of Chib and Zeng [14], which features little user-intervention and therefore helps avoid data mining.…”

“…In the Bayesian method of Chib and Zeng [14], test assets are irrelevant and we only need to focus on factors. Let F t � (f t , w t ) be return for n traded factors at time t, where f t is the pricing model that includes n f factors and w t is the remaining n w factors, n � n w + n f .…”

“…When the number of candidate factors is large, it is difficult to set the priors, and the results will be highly sensitive to the choice of priors. erefore, Chib and Zeng [14] adopt the commonly used multivariate t-distributions and inverse Wishart distribution as the priors of λ f and (Ω f , Ω w.f , Γ), respectively. Under this circumstance, we can obtain the prior distributions of (λ f , Ω f , Ω w.f , Γ) by specifying only three parameters and selecting a subsample of f t as the training sample.…”

“…We do not report the details of MCMC sampling here and refer the interested readers to the original paper. Following Chib and Zeng [14], we let ρ 0 be equal to n f + 6, s λ equal to 0.0025, and ] λ equal to 2.1. When sampling the priors, we repeat the sampling steps 80,000 times and discard the first 40,000 burn-in draws.…”