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

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Highly relevant for quantitative trading in several dimensions: (1) Accurate VaR estimation is critical for position sizing and risk limits; the 19.7% Gaussian underestimation has direct P&L implications. (2) The framework provides a principled way to incorporate behavioral biases into pricing models. (3) Out-of-sample forecasting improvements (81% cases) suggest practical trading signal improvements. (4) Directly applicable to portfolio risk management, regulatory compliance, and tail-risk hedging strategies. However, it is more of a distributional/risk modeling framework than a direct trading signal generator.

Moderate complexity. Core components (Student's t MLE, probability weighting transformation) are well-understood, but joint estimation of tail and behavioral parameters requires careful numerical optimization. The bounded transformation and Lipschitz conditions add theoretical constraints. However, the authors provide complete Python implementation via GitHub, reducing practical barriers. Understanding infinite divisibility preservation and the behavioral adjustment operator requires graduate-level probability theory.

Complete replication materials provided via GitHub repository including Jupyter notebooks, documentation, and instructions. Implementation uses Python 3.12.11 with standard statistical libraries. Executable on standard hardware within 2-3 hours. All estimation procedures, hypothesis tests, and backtesting routines are fully documented.