Resolving the Equity Premium Requires at Least Two Dimensions: A Machine-Checked Class No-Go and a Term-Structure Discriminator

Key Points

• This research aims to resolve the equity premium puzzle by introducing multiple dimensions in analysis. It challenges existing models by incorporating newer verification techniques.
• Utilizes a machine-checked class for formal verification with Lean 4 and Python scripts.
• Calibrates consumption-based asset pricing models to assess risk aversion and consumption smoothness.
• Examines equity premium prediction in the context of term-structure discrimination.
• Demonstrates that standard asset pricing models inadequately predict the equity premium under traditional measures.
• Reveals that incorporating more dimensions can significantly alter predictions and conclusions regarding equity premium.
• Highlights the necessity for formal verification in asset pricing models to enhance reliability and accuracy.