empfin 2.0

Empirical Finance Tools

empfin - Empirical Finance Tools in Python

empfin is a Python toolkit for empirical asset pricing models and risk premia estimation. This library is in active development and aims to implement models from all corners of the literature.

What's Inside

Currently available models for estimation of risk premia:

• TimeseriesReg: single-pass OLS time-series regression, described in Cochrane (2005), Section 12.1
• CrossSectionReg: two-pass cross-sectional regression, described in Cochrane (2005), Section 12.2
• NonTradableFactors: iterative maximum-likelihood estimator for non-tradable factors, described in Campbell, Lo & MacKinlay (2012), Section 6.2.3
• RiskPremiaTermStructure: term structure of risk premia with a single factor, tradable or not, following Bryzgalova, Huang & Julliard (2024). I would like to thank the authors for sharing their replication files.
• ConditionalRiskPremiaTermStructure: conditional, VAR-augmented version of the term-structure estimator, from Bryzgalova, Huang & Julliard (2024).
• Bayesian Fama-MacBeth Regressions from Bryzgalova, Huang & Julliard (2024):
  • BFM: Bayesian Fama-MacBeth (BFM-OLS), which replaces the two-pass point estimates with a posterior distribution over the risk premia
  • BFMGLS: GLS variant of the Bayesian Fama-MacBeth, which uses the idiosyncratic-error precision matrix in the cross-sectional step
  • BFMOMIT: variant of the Bayesian Fama-MacBeth that is robust to omitted factors by projecting onto the principal components of the asset-return covariance

Examples

For each model, there is a jupyter notebook with examples of their use.

Installation

pip install empfin

References

Bryzgalova, Huang, and Julliard (2024) “Bayesian Fama-MacBeth Regressions”) Working Paper

Bryzgalova, Huang, and Julliard (2024) “Macro Strikes Back: Term Structure of Risk Premia” Working Paper

Cochrane (2005) "Asset Pricing: Revised Edition". Princeton University Press.

Campbell, Lo, and MacKinlay (2012) "The Econometrics of Financial Markets"

Library Citation

Gustavo Amarante (2026). empfin - Empirical Finance Tools in Python. Retrieved from https://github.com/gusamarante/empfin