We present a convolutional variational autoencoder for cryptocurrency implied-volatility surfaces, together with a deployable predictor that combines it with a quadratic smile re-fit through a deterministic per-tenor routing rule. Trained on 6,034 fully-filled hourly Binance Options surfaces of BTC and ETH spanning May-October 2023 and parameterised on a common $6 \times 7$ tenor-delta grid, the model attains a hidden-cell surface-completion RMSE in the 0.94-1.56 vol-point range across both markets and mask rates 10-50%. The hybrid predictor attains 0.83 vol points at 50% masking against 7.00 for the smile re-fit alone, an eightfold reduction obtained at no additional inference cost. Under structurally-correlated hole patterns that emulate the withdrawal of an entire tenor of strikes, the smile re-fit incurs 9.6-13.1 vol points of error while the learned model remains at 1.5-1.9, isolating a regime in which the generative model is the only viable predictor. Joint training on BTC and ETH improves the in-distribution model on both markets by 9-27% relative to the better-performing single-symbol counterpart, indicating a substantially shared vol-surface manifold across the two largest cryptocurrencies over the observation window. The hybrid is calendar- and butterfly-arbitrage-free at the listed strikes, a property that the parametric smile re-fit alone fails at high mask rates. The per-snapshot reconstruction error of the trained model flags the late-October ETF-anticipation rally and the August $17$, $2023$ flash crash as elevated-error periods without supervision. All training and evaluation infrastructure is released to support reproducible follow-on work.

翻译：本文提出一种面向加密货币隐含波动率曲面的卷积变分自编码器，并搭配一种可部署预测器——该预测器通过确定性逐期限路由规则，将自编码器与二次微笑曲线重构相结合。模型基于2023年5月至10月期间BTC和ETH的6034张完全填充的Binance期权小时级波动率曲面（以$6 \times 7$的期限-Delta网格参数化）进行训练，在两种市场和10%-50%掩码率下，隐层单元的曲面补全均方根误差（RMSE）介于0.94-1.56波动率点之间。在50%掩码率下，混合预测器达到0.83波动率点，而单独使用微笑曲线重构的误差为7.00波动率点，在无需额外推理成本的前提下实现了八倍误差降低。针对模拟整条期限行权价缺失的结构性相关空洞模式，微笑曲线重构产生9.6-13.1波动率点误差，而学习模型误差仅维持在1.5-1.9波动率点，这揭示了生成模型作为唯一可行预测器的特殊场景。相较于表现更优的单标的模型，对BTC和ETH的联合训练将分布内模型在两种市场上的性能提升了9%-27%，表明观测窗口期内两种最大加密货币的波动率曲面流形存在显著共享性。混合模型在上市行权价处无日历套利与蝶式套利机会，而单独使用参数化微笑曲线重构在高掩码率下无法保持这一性质。训练模型对快照的重构误差可无监督地标记出异常高误差时段，包括10月下旬的ETF预期上涨行情以及2023年8月17日的闪崩事件。所有训练与评估基础设施均已开源，以支持后续可重复研究工作。