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

Ricardo Cris\'ostomo
Authors registered in the RePEc Author Service: Ricardo Crisóstomo

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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.

Date: 2020-12, Revised 2021-01
New Economics Papers: this item is included in nep-for and nep-upt
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Citations: View citations in EconPapers (1)

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Journal Article: Estimating real‐world probabilities: A forward‐looking behavioral framework (2021)
Working Paper: Estimating real word probabilities: a forward-looking behavioral framework (2021)
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