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 12.1CrossSectionReg: two-pass cross-sectional regression, described in Cochrane (2005), Section 12.2. Includes options for OLS and GLS estimators.NonTradableFactors: iterative maximum-likelihood estimator for non-tradable factors, described in Campbell, Lo & MacKinlay (2012), Section 6.2.3FamaMacBeth: classical two-pass Fama-MacBeth regression, with optional rolling-window first pass, Newey-West HAC standard errors, and Shanken (1992) errors-in-variables correction, from Fama & MacBeth (1973)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 premiaBFMGLS: GLS variant of the Bayesian Fama-MacBeth, which uses the idiosyncratic-error precision matrix in the cross-sectional stepBFMOMIT: 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"
Fama and MacBeth (1973) "Risk, Return, and Equilibrium: Empirical Tests" Journal of Political Economy, 81(3), 607-636
Library Citation
Gustavo Amarante (2026). empfin - Empirical Finance Tools in Python. Retrieved from https://github.com/gusamarante/empfin
1 maintainer
Release files for empfin 3.0
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