改进VAE模型，更准填补外汇期权隐含波动率空白
Filling in Missing FX Implied Volatilities with Uncertainties: Improving VAE-Based Volatility Imputation
- 调整VAE结构提升填补精度，无需复杂变体
- 在低缺失率下误差减半，优于传统方法
- 新增不确定性建模，输出结果带可信区间
金融数据中缺失值普遍存在，需通过插补方法填补。本文聚焦外汇期权隐含波动率的缺失值填补问题。已有研究采用变分自编码器（VAE）等神经网络方法，但经典模型如带跳跃的Heston模型表现更优。本文表明，仅对VAE架构进行简单修改即可显著提升插补性能（例如在低缺失率场景下，误差几乎减半），不再需要使用β-VAE。此外，我们改进了VAE插补算法，更好地处理数据不确定性，并获得插补值的准确置信度估计。
原文摘要 · Abstract (English)
Missing data is a common problem in finance and often requires methods to fill in the gaps, or in other words, imputation. In this work, we focused on the imputation of missing implied volatilities for FX options. Prior work has used variational autoencoders (VAEs), a neural network-based approach, to solve this problem; however, using stronger classical baselines such as Heston with jumps can significantly outperform their results. We show that simple modifications to the architecture of the VAE lead to significant imputation performance improvements (e.g., in low missingness regimes, nearly cutting the error by half), removing the necessity of using $β$-VAEs. Further, we modify the VAE imputation algorithm in order to better handle the uncertainty in data, as well as to obtain accurate uncertainty estimates around imputed values.
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