AI 中文总结
该研究提出套利感知潜在流匹配框架，经正则化变分自编码器压缩波动率曲面，结合流匹配模型生成符合无套利条件的隐含波动率曲面，在分布相似性、尾部保留等方面表现良好。
AI 中文摘要
我们提出了一种用于无条件隐含波动率曲面生成的套利感知潜在流匹配框架。该方法首先使用变分自编码器将高维曲面压缩到低维潜在空间，该变分自编码器由可微日历价差、看涨价差和蝶式套利惩罚项进行正则化。随后，流匹配模型学习将高斯先验分布迁移到经验潜在分布，生成的潜在样本被解码回波动率曲面。我们使用边际和曲面级Wasserstein距离、微笑和偏斜诊断、逐点分位数曲面、具有金融解释性的形状指标以及静态无套利测试来评估该方法。所提出的模型紧密复现了经验分布和主要的到期日-行权价结构，在极端Q99 regime中取得了最佳性能，且生成的曲面中有90.8%满足所有测试的静态无套利条件。总体而言，结果表明潜在流匹配在分布相似性、尾部保留和金融一致性之间实现了良好的平衡，且不需要采样后重加权。
英文摘要
We propose an arbitrage-aware latent flow-matching framework for unconditional implied volatility surface generation. The method first compresses high-dimensional surfaces into a low-dimensional latent space using a variational autoencoder regularized by differentiable calendar-spread, call-spread and butterfly-arbitrage penalties. A flow-matching model then learns to transport a Gaussian prior toward the empirical latent distribution, and generated latent samples are decoded back into volatility surfaces. We evaluate the approach using marginal and surface-level Wasserstein distances, smile and skew diagnostics, pointwise quantile surfaces, financially interpretable shape metrics, and static no-arbitrage tests. The proposed model closely reproduces the empirical distribution and the main maturity-moneyness structures, achieves the best performance in the extreme Q99 regime, and generates 90.8% of surfaces satisfying all tested static no-arbitrage conditions. Overall, the results show that latent flow matching provides a favorable balance between distributional similarity, tail preservation, and financial consistency without requiring post-sampling reweighting.
