  Since exchange economy considerably varies in the market assets, asset prices
have become an attractive research area for investigating and modeling
ambiguous and uncertain information in today markets. This paper proposes a new
generative uncertainty mechanism based on the Bayesian Inference and
Correntropy (BIC) technique for accurately evaluating asset pricing in markets.
This technique examines the potential processes of risk, ambiguity, and
variations of market information in a controllable manner. We apply the new BIC
technique to a consumption asset-pricing model in which the consumption
variations are modeled using the Bayesian network model with observing the
dynamics of asset pricing phenomena in the data. These dynamics include the
procyclical deviations of price, the countercyclical deviations of equity
premia and equity volatility, the leverage impact and the mean reversion of
excess returns. The key findings reveal that the precise modeling of asset
information can estimate price changes in the market effectively.
