Research Publication

This paper develops a new method informed by data and models to recover information about investor beliefs. Our approach uses information embedded in forward-looking asset prices in conjunction with asset pricing models. We step back from presuming rational expectations and entertain potential belief distortions bounded by a statistical measure of discrepancy. Additionally, our method allows for the direct use of sparse survey evidence to make these bounds more informative. Within our framework, market-implied beliefs may differ from those implied by rational expectations due to behavioral/psychological biases of investors, ambiguity aversion, or omitted permanent components to valuation. Formally, we represent evidence about investor beliefs using a novel nonlinear expectation function deduced using model-implied moment conditions and bounds on statistical divergence. We illustrate our method with a prototypical example from macro-finance using asset market data to infer belief restrictions for macroeconomic growth rates.

Paper

Associated Jupyter Code 

@article{chen2020robust,
  title={Robust identification of investor beliefs},
  author={Chen, Xiaohong and Hansen, Lars Peter and Hansen, Peter G},
  journal={Proceedings of the National Academy of Sciences},
  volume={117},
  number={52},
  pages={33130--33140},
  year={2020},
  publisher={National Acad Sciences}
}