Estimating real word probabilities: a forward-looking behavioral framework

Ricardo Crisóstomo

CNMV Working Papers from CNMV- Spanish Securities Markets Commission - Research and Statistics Department

Abstract: 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.

Keywords: Sentiment; density forecasts; pricing kernel; options data; behavioral finance (search for similar items in EconPapers)
JEL-codes: C14 C52 C53 G12 G13 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-for
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Journal Article: Estimating real‐world probabilities: A forward‐looking behavioral framework (2021)
Working Paper: Estimating real-world probabilities: A forward-looking behavioral framework (2021)
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