Estimating Real-world Probabilities: a Forward-looking Behavioral Framework
35 Pages Posted: 18 Dec 2020 Last revised: 26 Jan 2021
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
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