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

2020/12/16 by Ricardo Crisóstomo, Crisóstomo, Ricardo
Decision Sciences · Economics, Econometrics and Finance · #FOS: Economics and business #Forecasting Techniques and Applications #Market Dynamics and Volatility #Monetary Policy and Economic Impact #Pricing of Securities (q-fin.PR) #Risk Management (q-fin.RM) #Statistical Finance (q-fin.ST) #q-fin.PR #q-fin.RM #q-fin.ST

paper · pdf · doi:10.48550/arxiv.2012.09041

openalex publication_date 2020/12/16 · arxiv created 2021/01/25 · arxiv updated 2021/01/26 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28

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. Further analyses confirm that our real-world densities outperform densities recalibrated to avoid past mistakes and improve predictive models where risk aversion is dynamically estimated from option prices.

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