发表机构
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种通用条件扩散模型，联合生成次日IVS增量和股票收益，在50只股票上训练并在样本内外测试，优于VolGAN基准，证明跨股票共享动态可行。
AI 中文摘要
对期权隐含波动率曲面（IVS）动态进行建模对于期权组合的定价、对冲和风险管理至关重要。我们开发了一个通用条件扩散模型，该模型学习联合生成次日IVS增量以及标的股票的收益。该模型在来自50只股票的汇总数据上进行训练，并在一个测试集上进行评估，该测试集包含50只样本内股票和50只训练中未包含的样本外股票。训练目标主要是最小化均方误差（MSE），其变体还联合施加曲面平滑性约束，并对静态套利的存在进行惩罚，这些都是具有经济意义的约束。在样本内和样本外股票中，我们的扩散模型在减少套利违规、改善股票风险预测以及使前三个主成分解释的方差比率对齐方面均优于VolGAN基准。该模型能够很好地外推到训练中未包含的股票，这一事实表明学习到的动态可以在股票之间共享。这些发现支持通用条件扩散作为多股票隐含波动率曲面情景生成的可靠框架。
英文摘要
Modeling the dynamics of option implied volatility surface (IVS) is crucial for pricing, hedging, and risk-managing option portfolios. We develop a universal conditional diffusion model that learns to jointly generate next-day IVS increments and the underlying stock's returns. The model is trained on pooled data from 50 stocks and evaluated on a test set comprising 50 in-sample stocks and 50 out-of-sample stocks excluded from training. The training objective is primarily the minimization of MSE, with the variants that jointly impose surface smoothness, and that penalize the presence of static-arbitrage, which are all economically meaningful constraints. Across in-sample and out-of-sample stocks, our diffusion model outperforms the VolGAN benchmark in reducing arbitrage violations, improving stock risk prediction, and aligning explained variance ratios by the first three principal components. The fact that the model extrapolates well to stocks that are excluded from training indicates that the learned dynamics can be shared across stocks. These findings support a universal conditional diffusion as a credible framework for multi-stock implied volatility surface scenario generation.
Comments9 pages, 5 figures, 4 tables; updated the references. Code is available at https://github.com/mzyang-code/Universal-Diffusion-for-IVS
