用自动验证框架发现大模型生成期权定价代码的缺陷并改进方法
RIDGE: An Autonomous Framework for Validation and Method Discovery in LLM-Generated Option Pricing
- 通过无套利测试、压力测试等多维度自动验证代码
- 在5个随机波动率模型中修复所有缺陷，发现2处方法论局限
- 适合量化金融开发者和大模型应用研究者参考
自动化代码生成正成为量化金融的重要工具，大语言模型可直接从数学模型规范生成期权定价实现。然而，验证此类实现远超传统软件测试：数值定价方法必须保持数学一致性、数值稳定性，并在广泛参数范围内可靠。我们提出RIDGE，一个自主验证框架，对生成的定价实现进行结构化的无套利测试、压力测试、基准对比和一致性检查。验证结果被诊断性解读，知识积累于仓库并在不同模型及后续验证迭代中复用。这实现了定价实现与验证方法的系统性优化。该框架应用于五个随机波动率模型，在所有案例中均修复了检测到的实现缺陷；在两个案例中揭示了方法论局限，推动了替代数值方法的开发。补充材料见GitHub仓库：https://github.com/ShQiangLiu/ridge。
原文摘要 · Abstract (English)
Automated code generation is becoming an important tool in quantitative finance, where large language models can generate option pricing implementations directly from mathematical model specifications. Validating such implementations, however, requires considerably more than conventional software testing: numerical pricing methods must remain mathematically consistent, numerically stable, and reliable across a wide range of model parameters. We introduce RIDGE, an autonomous validation framework in which generated pricing implementations are subjected to structured no-arbitrage tests, stress tests, benchmark comparisons, and consistency checks. Validation evidence is interpreted diagnostically, while the resulting knowledge is accumulated in a repository and reused across models and successive validation iterations. This enables systematic refinement of both the pricing implementation and the validation methodology. The framework is applied to five stochastic volatility models. Across these studies, all detected implementation defects are removed and, in two cases, the validation process reveals methodological limitations and motivates the development of alternative numerical methods. The supplementary material is available in the GitHub repository: https://github.com/ShQiangLiu/ridge.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。