Paper: arXiv 2401.11958
Authors: Daniel Kršek, Gudmund Pammer
Abstract
We investigate duality and existence of dual optimizers for several adapted optimal transport problems under minimal assumptions. This includes the causal and bicausal transport, the causal and bicausal barycenter problem, and a multimarginal problem incorporating causality constraints. Moreover, we characterize polar sets in the causal and bicausal setting and discuss applications of our results in robust finance. We consider a non-dominated model of several financial markets where stocks are traded dynamically, but the joint stock dynamics are unknown. We show that a no-arbitrage assumption naturally leads to sets of multicausal couplings. Consequently, computing the robust superhedging price is equivalent to solving an adapted transport problem, and finding a superhedging strategy means solving the corresponding dual.
Complexity vs Empirical Score
- Math Complexity: 9.2/10
- Empirical Rigor: 1.5/10
- Quadrant: Lab Rats — theoretically deep, empirically untested
Why this score: This paper presents highly sophisticated theoretical work on adapted optimal transport, dual attainment, and polar set characterization with applications to model-independent superhedging. While math complexity and theoretical novelty are exceptionally high, it is a purely theoretical paper lacking empirical data, backtests, or code implementation.
Research Flowchart
flowchart TD
A["Research Goal: Establish duality & dual attainment for adapted optimal transport under minimal assumptions"] --> B["Input: Non-dominated market models, dynamic stock trading, & causality constraints"]
B --> C["Core Methodology: Analyze causal/bicausal transport, barycenter problems, & characterize polar sets"]
C --> D["Application & Computation: Map robust financial superhedging to adapted transport duality"]
D --> E["Key Outcomes: Duality holds for adapted transport; robust superhedging price equals optimal transport cost & dual strategy exists"]
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and 42 more
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3 of 50 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.