Paper: arXiv 2603.08552

Authors: Emanuele Borgonovo, An Chen, Massimo Marinacci, Shihao Zhu

Abstract

We study continuous-time portfolio choice with nonlinear payoffs under smooth ambiguity and Bayesian learning. We develop a general framework for dynamic, non-concave asset allocation that accommodates nonlinear payoffs, broad utility classes, and flexible ambiguity attitudes. Dynamic consistency is obtained by a robust representation that recasts the ambiguity-averse problem as ambiguity-neutral with distorted priors. This structure delivers explicit trading rules by combining nonlinear filtering with the martingale approach and nests standard concave and linear-payoff benchmarks. As a leading application, delegated management with convex incentives illustrates that ambiguity aversion shifts beliefs toward adverse states, limits the range of states that would otherwise trigger more aggressive risk taking, and reduces volatility through lower risky exposure.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper presents a rigorous theoretical framework combining smooth ambiguity, Bayesian learning, and non-concave optimization via concavification and martingale methods. However, it is primarily analytical and mathematical, lacking empirical backtesting or real market data validation.

Research Flowchart

  flowchart TD
    A[Research Goal: Solve dynamic non-concave portfolio choice under smooth ambiguity & Bayesian learning] --> B[Inputs: Non-linear payoffs, multi-asset prices, prior beliefs & ambiguity attitudes]
    B --> C[Method 1: Robust representation for dynamic consistency via distorted priors]
    C --> D[Method 2: Non-linear filtering combined with martingale approach]
    D --> E[Computational Output: Explicit trading rules & continuous-time optimal asset allocation]
    E --> F[Key Outcome: Ambiguity aversion shifts beliefs to adverse states & reduces risk taking]

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3 of 77 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.