AI 中文总结
该研究针对隐含波动率曲面预测难题，提出解耦生成优化框架，用条件扩散模型捕捉随机动态，通过曲面感知注意力模块优化无套利约束，在沪深300股指期权的日度和分钟级预测中提升了精度并降低了残差违规
AI 中文摘要
隐含波动率（IV）曲面预测对期权定价、对冲及风险管理至关重要，但由于未来曲面具有随机性，而定价输入需满足静态无套利形态约束，该任务仍具挑战性。我们提出一种解耦生成优化框架，将隐含波动率曲面（IVS）预测视为操作风险曲面建模问题。第一阶段采用条件扩散模型学习未来曲面的条件分布，生成的集成结果捕捉预测分布变化，其中位数为后续优化提供稳健的代表性曲面；第二阶段引入曲面感知注意力模块（SAAM），这是一种截面优化算子，可提升代表性曲面对市场观测值的拟合度及静态无套利诊断效果。该设计将分布学习与曲面优化分离，使扩散模型能捕捉随机市场动态，同时SAAM控制最终曲面的静态无套利残差违规。我们在2020年6月至2024年9月的沪深300股指期权上，按日度和分钟级预测协议评估该框架。扩散阶段提升了预测精度，生成的预测区间随期权价值状态（moneyness）、到期期限和采样频率变化；优化阶段提高了对市场观测值的拟合精度，减少了测得的静态无套利残差违规，且在分钟级场景下增益更显著。注意力诊断显示，SAAM执行的是自适应截面优化，而非固定局部平滑
英文摘要
Implied volatility surface forecasting is essential for option valuation, hedging,and risk management, but remains difficult because future surfaces are stochastic while pricing inputs must satisfy static no-arbitrage shape restrictions. We propose a decoupled generative refinement framework for IVS forecasting as an operational risk surface modeling problem. The first stage uses a conditional diffusion model to learn the conditional distribution of future surfaces. The generated ensemble captures predictive distributional variation, and its median provides a robust representative surface for subsequent refinement. The second stage introduces a Surface Aware Attention Module (SAAM), a cross sectional refinement operator that improves fit to market observations and staticno-arbitrage diagnostics for the representative surface. This design separates distribution learning from surface refinement, allowing the diffusion model to capture stochastic market dynamics while SAAM controls static no-arbitrage residual violations on the final surface. We evaluate the framework on CSI 300 index options from June 2020 to September 2024 under daily and minute level forecasting protocols. The diffusion stage improves forecasting accuracy and produces predictive intervals that vary across moneyness, maturity, and sampling frequency. The refinement stage improves fitting accuracy against market observations and reduces measured static no-arbitrage residual violations, with stronger gains at the minute level. Attention diagnostics suggest that SAAM performs adaptive cross sectional refinement rather than fixed local smoothing
Comments35 pages
