Conditional Asset Pricing Models via Machine Learnings for the Chinese Stock Market

Motivated by the institutional features and distinctive return dynamics of the Chinese stock market, this dissertation develops a unified conditional asset pricing framework that integrates time-varying risk exposures, distributional asymmetry, and high-dimensional information. The analysis addresses three interrelated challenges in empirical asset pricing: state-dependent factor loadings, cross-sectional dependence driven by latent common shocks, and nonlinear relationships between returns and conditioning information in high-dimensional environments.

Name thesis:degree_name

Ph.D.

Discipline thesis:degree_discipline

Economics

Grantor dc:publisher

University of Kansas

Year dc:date.issued

2026

Author dc:creator

• Jin, Jingwei

Advisor dc:contributor.advisor

• Cai, Zongwu

• Chinese stock market
• conditional asset pricing
• conditional CAPM
• conditional quantile model
• high-dimensional factors
• machine learning

Language dc:language.iso

en

Dc Identifier Other

https://www.proquest.com/LegacyDocView/DISSNUM/32696511

OAI identifier oai:identifier

oai:kuscholarworks.ku.edu:1808/39491