考虑套利的隐含波动率曲面多步预测：使用潜在扩散模型对曲面轨迹进行建模
Arbitrage-Aware Multi-Step Forecasting of Implied Volatility Surfaces: Modelling Surface Trajectories Using Latent Diffusion
- University of St.Gallen（圣加仑大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对隐含波动率曲面的多步预测问题，提出条件潜在扩散框架，结合考虑套利的自编码器与扩散模型，在SPX数据集上生成逼真多步场景且点预测性能优于持续性基准。
AI中文摘要：
隐含波动率曲面汇总了期权市场，是许多金融应用的核心。预测其未来演化需要对二维几何、时间依赖性和预测不确定性进行建模，同时保持经济上的可容许性。我们提出了一种条件潜在扩散框架，用于生成隐含波动率曲面和标的收益的联合30步轨迹。考虑套利的自编码器学习低维曲面表示，而扩散模型则捕捉条件联合演化。在SPX曲面上进行评估，该框架生成了逼真的概率多步场景，同时在点预测方面也优于持续性基准。
英文摘要：
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.
