Accurate Evaluation of Asset Pricing Under Uncertainty and Ambiguity of Information

TL;DR

This paper introduces a Bayesian inference and correntropy-based method for accurately evaluating asset prices under market uncertainty and ambiguity, capturing complex dynamics like volatility and leverage effects.

Contribution

It presents a novel BIC technique that models risk, ambiguity, and information variations in asset pricing, improving estimation accuracy in uncertain markets.

Findings

Effective estimation of asset price changes

Model captures volatility and leverage effects

Improved understanding of market dynamics

Abstract

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.