用隐扩散模型预测波动率曲面30步未来走势，兼顾市场套利约束。
Arbitrage-Aware Multi-Step Forecasting of Implied Volatility Surfaces: Modelling Surface Trajectories Using Latent Diffusion
- 基于隐变量扩散模型联合建模曲面与标的资产收益的30步演化路径。
- 在标普500波动率曲面上生成的多步概率情景符合经济合理性且优于基准。
- 通过套利感知自编码器压缩曲面维度，提升建模效率与真实性。
隐含波动率曲面总结了期权市场的信息，是众多金融应用的核心。预测其未来演变需同时建模二维几何结构、时间依赖性及预测不确定性，并保证经济合理性。本文提出一种条件隐扩散框架，用于生成隐含波动率曲面与底层资产收益的联合30步轨迹。一个套利感知自编码器学习低维曲面表示，而扩散模型则捕捉条件下的联合演化过程。在标普500（SPX）曲面上评估显示，该框架生成的真实概率多步场景优于持久性基准，在点预测上也表现更优。
原文摘要 · Abstract (English)
Implied volatility surfaces summarise the option market and are central to many financial applications. Forecasting their future evolution requires modelling two-dimensional geometry, temporal dependence, and predictive uncertainty while preserving economic admissibility. We propose a conditional latent diffusion framework for generating joint 30-step trajectories of implied volatility surfaces and underlying returns. An arbitrage-aware autoencoder learns a low-dimensional surface representation, while the diffusion model captures the conditional joint evolution. Evaluated on SPX surfaces, the framework generates realistic probabilistic multi-step scenarios while also outperforming the persistence benchmark in point forecasting.
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。