Estimating real‐world probabilities: A forward‐looking behavioral framework

Abstract

We show that disentangling sentiment-induced biases from fundamental expectations significantly improves the accuracy and consistency of probabilistic forecasts. Using data from 1994 to 2017, we analyze 15 stochastic models and risk-preference combinations and in all possible cases a simple behavioral transformation delivers substantial forecast gains. Our 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. We also implement a trading strategy that shows how behavioral biases can be exploited to generate trading profits. Further analyses confirm that our real-world densities outperform forecasts recalibrated to avoid past mistakes and improve predictive models where risk aversion is dynamically estimated from option prices.

International evidence of the forecasting ability of option‐implied distributions

“…Other papers studying the explanatory power of risk‐adjusted densities include Shackleton et al (2010) for the S&P500 and Høg and Tsiaras (2011) on crude oil. Crisóstomo (2021) also considers investors' sentiment in the context of the Spanish IBEX35 index.…”