rafe: Decision-Aligned Forecast Evaluation and Moment Correction

Evaluates return forecasts by the economic damage their errors cause rather than by their size, and corrects the forecast moments that a mean-variance optimiser is most sensitive to.

Details

Conventional accuracy measures weight every asset equally; a portfolio optimiser does not. The risk-adjusted forecast error (RAFE) and the operator-norm covariance forecast error (C-RAFE) reweight forecast errors by the risk metric the decision actually uses, and combine into an upper bound on the Sharpe-ratio gap of the plug-in portfolio (T-RAFE).

Two groups of functions:

• Metrics: compute_rafe(), compute_crafe(), compute_trafe(), and restrict_cov() for the nested sequence of covariance restrictions that reduces RAFE to RMSE.

• Moment correction: mu_rafe_stein() and sigma_crafe_floor() act on the two channels of the bound; sep_tune() and joint_trafe_tune() select their intensities on an inner-validation split.

The ff12 dataset, the universe of the published paper, makes every example reproducible offline.

References

Salcher, L., Stöckl, S., & Hanke, M. (2026). Lost in Translation? Risk-Adjusting RMSE for Economic Forecast Performance. Journal of Forecasting. doi:10.1002/for.70134

Stöckl, S., Salcher, L., & Hanke, M. Post-Optimal Moment Correction for Mean-Variance Portfolios. Working paper.

See also

Useful links:

• https://github.com/sstoeckl/rafe

• https://www.sebastianstoeckl.com/rafe/

• Report bugs at https://github.com/sstoeckl/rafe/issues

Author

Maintainer: Sebastian Stöckl sebastian.stoeckl@uni.li (ORCID)