Physics-informed convolutional transformer for predicting volatility surface. (English) Zbl 1537.91328
Summary: 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.
MSC:
| 91G20 | Derivative securities (option pricing, hedging, etc.) |
| 68T07 | Artificial neural networks and deep learning |
Keywords:
volatility; Black-Scholes model; physics-informed neural networks; convolutional LSTM; attention mechanism; convolutional transformerReferences:
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