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

TL;DR

This paper introduces an econometric framework combining heavy-tailed Student's t distributions with behavioral probability weighting, demonstrating improved asset pricing models that outperform Gaussian assumptions in real-world data.

Contribution

It develops a novel econometric approach integrating heavy tails and behavioral biases, with empirical validation on extensive financial data.

Findings

Student's t models outperform Gaussian models in 88.4% of cases

Our models underestimate 99% VaR by only 3.2%

Bounded probability weighting preserves key mathematical properties

Abstract

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.