Convergence of Sinkhorn's Algorithm for Entropic Martingale Optimal Transport Problem

• 成果类型: Article; Early Access
• 署名作者: Chen, Fan; Conforti, Giovanni; Ren, Zhenjie; Wang, Xiaozhen
• 署名单位: Shanghai Jiao Tong University; University of Padua; Universite PSL; Universite Paris-Dauphine
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X; 1526-5471
• DOI: 10.1287/moor.2024.0619
• 发表日期: 2026-02-16
• 关键词: entropic martingale optimal transport; Sinkhorn's algorithm; stochastic volatility models; calibration problems; scaling algorithms; options; volatility; variance; Duality

摘要

In this paper, we study the entropic martingale optimal transport (EMOT) problem on R. The investigation of the EMOT problem arises in the calibration problem of the stochastic volatility models, where martingale constraints reflect no-arbitrage pricing conditions under the risk-neutral measure, as originally proposed by Henry-Laborde`re. We first establish the dual formulation of the EMOT problem and prove that Sinkhorn's algorithm achieves an exponential convergence rate under mild conditions. Notably, our analysis does not presuppose the existence of optimal potentials and rigorously confirms the absence of a primal-dual gap. These results provide a theoretical foundation for solving EMOT via Sinkhorn's method and constructing the optimal distribution from dual coefficients.