Estimating Real-world Probabilities: a Forward-looking Behavioral Framework

35 Pages Posted: 18 Dec 2020 Last revised: 26 Jan 2021

See all articles by Ricardo Crisóstomo

Ricardo Crisóstomo

Comisión Nacional del Mercado de Valores (CNMV); National Distance Education University (UNED)

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Date Written: November 17, 2020


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.

Keywords: Sentiment, density forecasts, pricing kernel, options data, behavioral finance

JEL Classification: C14, C52, C53, G12, G13

Suggested Citation

Crisóstomo, Ricardo, Estimating Real-world Probabilities: a Forward-looking Behavioral Framework (November 17, 2020). Available at SSRN: or

Ricardo Crisóstomo (Contact Author)

Comisión Nacional del Mercado de Valores (CNMV) ( email )

C/ Edison, 4
Madrid, Madrid 28006

National Distance Education University (UNED)

Calle Bravo Murillo, 38
Madrid, Madrid 28015

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