Estimating real word probabilities: a forward-looking behavioral framework

This document shows that disentangling sentiment-induced biases from fundamental expectations significantly improves the accuracy and consistency of probabilistic forecasts. Using data from 1994 to 2017, 15 stochastic models and risk-preference combinations are analyzed and in all possible cases a simple behavioral transformation delivers substantial forecast gains. The results are robust across different evaluation methods, risk-preference hypotheses and sentiment calibrations, demonstrating that behavioral effects can be effectively used to forecast asset prices. Further analyses confirm that the real-world densities outperform densities recalibrated to avoid past mistakes and improve predictive models where risk aversion is dynamically estimated from option prices.