Hybrid LLM and Higher-Order Quantum Approximate Optimization for CSA Collateral Management

ArXiv ID: 2510.26217 “View on arXiv”

Authors: Tao Jin, Stuart Florescu, Heyu, Jin

Abstract

We address finance-native collateral optimization under ISDA Credit Support Annexes (CSAs), where integer lots, Schedule A haircuts, RA/MTA gating, and issuer/currency/class caps create rugged, legally bounded search spaces. We introduce a certifiable hybrid pipeline purpose-built for this domain: (i) an evidence-gated LLM that extracts CSA terms to a normalized JSON (abstain-by-default, span-cited); (ii) a quantum-inspired explorer that interleaves simulated annealing with micro higher order QAOA (HO-QAOA) on binding sub-QUBOs (subset size n <= 16, order k <= 4) to coordinate multi-asset moves across caps and RA-induced discreteness; (iii) a weighted risk-aware objective (Movement, CVaR, funding-priced overshoot) with an explicit coverage window U <= Reff+B; and (iv) CP-SAT as single arbiter to certify feasibility and gaps, including a U-cap pre-check that reports the minimal feasible buffer B*. Encoding caps/rounding as higher-order terms lets HO-QAOA target the domain couplings that defeat local swaps. On government bond datasets and multi-CSA inputs, the hybrid improves a strong classical baseline (BL-3) by 9.1%, 9.6%, and 10.7% across representative harnesses, delivering better cost-movement-tail frontiers under governance settings. We release governance grade artifacts-span citations, valuation matrix audit, weight provenance, QUBO manifests, and CP-SAT traces-to make results auditable and reproducible.

Keywords: collateral optimization, ISDA CSA, quantum annealing (QAOA), CP-SAT, subset sum QUBO, Derivatives (Collateral)

Complexity vs Empirical Score

• Math Complexity: 9.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics including higher-order QAOA Hamiltonians (k≤4), CVaR linearization, and multi-constraint encoding, indicating high complexity. It also presents empirical results on government bond datasets with specific performance improvements (9.1%, 9.6%, 10.7%), releases governance-grade artifacts, and uses CP-SAT for certification, demonstrating significant implementation and backtest readiness.

  flowchart TD
    G["Research Goal: CSA Collateral Optimization"] --> I["Input: ISDA CSAs, Gov Bonds, Multi-CSA Data"]
    I --> M1["LLM Term Extraction<br>Abstain-by-default JSON"]
    M1 --> M2["HO-QAOA Exploration<br>High-Order QUBOs n<=16"]
    M2 --> M3["CP-SAT Certification<br>Feasibility & Gap Analysis"]
    M3 --> F["Outcomes: 9-10% Improvement<br>Auditable Artifacts & Governance Grade"]