Representation of Forward Performance Criteria with Random Endowment via FBSDE and Its Application to Forward Optimized Certainty Equivalent

ABSTRACT We extend the notion of forward performance criteria to settings with random endowment in incomplete markets. Building on these results, we introduce and develop the novel concept of forward optimized certainty equivalent (forward OCE) , which offers a genuinely dynamic valuation mechanism that accommodates progressively adaptive market model updates, stochastic risk preferences, and incoming claims with arbitrary maturities. In parallel, we develop a new methodology to analyze the emerging stochastic optimization problems by directly studying the candidate optimal control processes for both the primal and dual problems. Specifically, we derive two new systems of forward‐backward stochastic differential equations (FBSDEs) and establish necessary and sufficient conditions for optimality, and various equivalences between the two problems. We provide representative examples for forward performance criteria with random endowment and forward OCE. For the case of exponential criteria, we investigate the connection between forward OCE and forward entropic risk measure.

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Publication Details

Journal
Mathematical Finance
Published
2026-08-25
DOI
https://doi.org/10.1111/mafi.70050
Primary Topic
Risk and Portfolio Optimization
Type
article
Field-Weighted Citation Impact
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article

Representation of Forward Performance Criteria with Random Endowment via FBSDE and Its Application to Forward Optimized Certainty Equivalent

Mathematical Finance
Risk and Portfolio Optimization
article

Representation of Forward Performance Criteria with Random Endowment via FBSDE and Its Application to Forward Optimized Certainty Equivalent

article en

Abstract

ABSTRACT We extend the notion of forward performance criteria to settings with random endowment in incomplete markets. Building on these results, we introduce and develop the novel concept of forward optimized certainty equivalent (forward OCE) , which offers a genuinely dynamic valuation mechanism that accommodates progressively adaptive market model updates, stochastic risk preferences, and incoming claims with arbitrary maturities. In parallel, we develop a new methodology to analyze the emerging stochastic optimization problems by directly studying the candidate optimal control processes for both the primal and dual problems. Specifically, we derive two new systems of forward‐backward stochastic differential equations (FBSDEs) and establish necessary and sufficient conditions for optimality, and various equivalences between the two problems. We provide representative examples for forward performance criteria with random endowment and forward OCE. For the case of exponential criteria, we investigate the connection between forward OCE and forward entropic risk measure.

Mathematical Finance
University of Warwick (GB), Science Oxford (GB), Ocean University of China (CN), The University of Texas at Austin (US)
Openalex Percentile: Top 99%
Risk and Portfolio Optimization
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