Probability Weighting Meets Heavy Tails: An Econometric Framework for Behavioral Asset Pricing

We develop an econometric framework integrating heavy-tailed Student’s $t$ distributions with behavioral probability weighting while preserving infinite divisibility. Using 432{,}752 observations across 86 assets (2004–2024), we demonstrate Student’s $t$ specifications outperform Gaussian models in 88.4% of cases. Bounded probability-weighting transformations preserve mathematical properties required for dynamic pricing. Gaussian models underestimate 99% Value-at-Risk by 19.7% versus 3.2% for our specification. Joint estimation procedures identify tail and behavioral parameters with established asymptotic properties. Results provide robust inference for asset-pricing applications where heavy tails and behavioral distortions coexist.