Comparison of Bayesian model selection criteria and conditional Kolmogorov test as applied to spot asset pricing models

We compare Bayesian and sample theory model specification criteria. For the Bayesian criteria we use the deviance information criterion and the cumulative density of the mean squared errors of forecast. For the sample theory criterion we use the conditional Kolmogorov test. We use Markov chain Monte Carlo methods to obtain the Bayesian criteria and bootstrap sampling to obtain the conditional Kolmogorov test. Two non-nested models we consider are the CIR and Vasicek models for spot asset prices. Monte Carlo experiments show that the DIC performs better than the cumulative density of the mean squared errors of forecast and the CKT. According to the DIC and the mean squared errors of forecast, the CIR model explains the daily data on uncollateralized Japanese call rate from January 1 1990 to April 18 1996; but according to the CKT, neither the CIR nor Vasicek models explains the daily data. -- Deviance information criterion ; Cumulative density of the mean squared errors of forecast ; Markov chain Monte Carlo algorithms ; Block bootstrap ; Generalized methods of moments ; Conditional Kolmogorov test ; CIR and Vasicek models