{
  "annotation_id": "31b36519-153d-42f9-bfc4-f3ac629cc699",
  "date_created": "2026-02-17T05:53:03.731000Z",
  "date_modified": "2026-02-17T05:53:03.731000Z",
  "file_hash": "2b62c52431eaa3c4c9aca2f76f77572ed5c208a0883d4fb7505fdc7caceb664c",
  "private": false,
  "record": {
    "abstract": "This paper develops a computational framework for Multi-Period Martingale Optimal Transport (MMOT), addressing convergence rates, algorithmic efficiency, and financial calibration. Our contributions include: (1) Theoretical analysis: We establish discrete convergence rates of $O(\\sqrt{\\Delta t} \\log(1/\\Delta t))$ via Donsker\u0027s principle and linear algorithmic convergence of $(1-\\kappa)^{2/3}$; (2) Algorithmic improvements: We introduce incremental updates ($O(M^2)$ complexity) and adaptive sparse grids; (3) Numerical implementation: A hybrid neural-projection solver is proposed, combining transformer-based warm-starting with Newton-Raphson projection. Once trained, the pure neural solver achieves a $1{,}597\\times$ online inference speedup ($4.7$s $\\to 2.9$ms) suitable for real-time applications, while the hybrid solver ensures martingale constraints to $10^{-6}$ precision. Validated on 12,000 synthetic instances (GBM, Merton, Heston) and 120 real market scenarios.",
    "arxiv_id": "2601.05290",
    "authors": [
      "Sri Sairam Gautam B"
    ],
    "categories": [
      "q-fin.CP",
      "q-fin.MF",
      "q-fin.PR"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Multi-Period Martingale Optimal Transport: Classical Theory, Neural Acceleration, and Financial Applications",
    "url": "https://arxiv.org/abs/2601.05290",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "2ccb58bb-4425-4af2-bba8-85a2993f492c",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}