发表机构
Mathematical Institute, Utrecht University; School of Mathematical Sciences, Peking University; Delft Institute of Applied Mathematics, Delft University of Technology（乌得勒支大学数学研究所; 北京大学数学科学学院; 代尔夫特理工大学应用数学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大语言模型生成的期权定价实现，介绍自主验证框架RIDGE，通过多种测试和检查优化定价实现与验证方法，应用于五个随机波动率模型，消除缺陷并带来新定价方法。
AI 中文摘要
自动代码生成正成为定量金融中的重要工具，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，验证此类实现所需远超传统软件测试：数值定价方法必须在数学上保持一致、数值稳定且在广泛模型参数范围内可靠。我们引入了RIDGE，一个自主验证框架，对生成的定价实现进行结构化无套利测试、压力测试、基准比较和一致性检查。验证证据经诊断性解读，所得知识存储在知识库中并跨模型和连续验证迭代复用。这使得定价实现和验证方法得以系统优化。该框架应用于五个随机波动率模型。在这些研究中，所有检测到的实现缺陷均被消除，在两个案例中，验证过程本身还带来了新的半解析定价方法。补充材料可在GitHub仓库获取：此https链接。
英文摘要
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.
Comments33 pages
