Paper: arXiv 2502.13744

Authors: Ken Kangda Wren

Abstract

Existing approaches to asset-pricing under model-uncertainty adapt classical utility-maximization frameworks and seek theoretical comprehensiveness. We move toward practice by considering binary model-risks and by emphasizing ‘constraints’ over ‘preference’. This decomposes viable economic asset-pricing into that of model and non-model risks separately, leading to a unique and convenient model-risk pricing formula. Its parameter, a dynamically conserved constant of model-risk inference, allows an integrated representation of ex-ante risk-pricing and bias such that their ex-post impacts are disentangled via well-known anomalies, Momentum and Low-Risk, whose risk-reward patterns acquire a fresh significance: peak-reward reveals ex-ante risk-premia, and peak-location, bias.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 2.5/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper provides a rigorous mathematical derivation of risk-neutral equivalent pricing under binary model-uncertainty using continuous-time diffusion processes and filtration theory. However, it is primarily a theoretical exploration with minimal empirical backtesting or data validation provided in the text. The novel perspective on separating model risk via dynamic constraints rather than recursive preferences offers interesting theoretical insights into asset pricing anomalies.

Research Flowchart

  flowchart TD
    A[Research Goal: Practical Asset Pricing<br/>Under Binary Model Uncertainty] --> B[Methodology: Shift Focus from<br/>Preferences to Portfolio Constraints]
    B --> C[Input Data: Ex-Ante Model Risks<br/>& Market Returns - Equities]
    C --> D[Process: Decompose Pricing into<br/>Model vs Non-Model Risk Components]
    D --> E[Outcome: Unique Model-Risk Formula<br/>with Conserved Inference Parameter]
    E --> F[Empirical Validation: Disentangle Ex-Post Risk<br/>& Bias via Momentum & Low-Risk Anomalies]

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0 of 22 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.