Physics-informed convolutional transformer for predicting volatility surface
提出一种结合物理信息神经网络与卷积Transformer的新架构，用于预测金融市场的波动率曲面，数值实验表明其性能优于ConvLSTM等现有深度学习方法。
Predicting volatility is important for asset predicting, option pricing and hedging strategies because it cannot be directly observed in the financial market. The dynamics of the volatility surface is difficult to estimate. In this paper, we establish a novel architecture based on physics-informed neural networks and convolutional transformers. The performance of the new architecture is directly compared to other well-known deep-learning architectures, such as standard physics-informed neural networks, convolutional long-short term memory (ConvLSTM), and self-attention ConvLSTM. Numerical evidence indicates that the proposed physics-informed convolutional transformer network achieves a superior performance than other methods.