Arbitrage-Aware Multi-Step Forecasting of Implied Volatility Surfaces: Modelling Surface Trajectories Using Latent Diffusion

2026-08-23

AI Summary

该研究通过使用条件潜扩散框架和无套利自编码器，解决了对未来隐含波动率曲面的预测问题。

Abstract

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.

Problem

• Implied Volatility Surfaces
• Forecasting
• Arbitrage-Aware
• Temporal Dependence
• Predictive Uncertainty

Innovation

• conditional latent diffusion
• arbitrage-aware autoencoder
• implied volatility surfaces
• multi-step forecasting