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

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  • Akash Deep
  • Svetlozar T. Rachev
  • Frank J. Fabozzi

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.

Suggested Citation

  • Akash Deep & Svetlozar T. Rachev & Frank J. Fabozzi, 2025. "Probability Weighting Meets Heavy Tails: An Econometric Framework for Behavioral Asset Pricing," Papers 2511.16563, arXiv.org.
    • Handle: RePEc:arx:papers:2511.16563
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    File URL: http://arxiv.org/pdf/2511.16563
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