The remarkable performance of deep learning is based on its ability to learn high-level features by processing large amounts of data. This exceptionally superior performance has attracted the attention of researchers studying option pricing. However, option data are more expensive and less accessible than other types of data and are imbalanced because of the liquidity of options. This motivated us to propose a new option pricing and delta-hedging framework called DeepOption. This framework, which is based on deep learning, can improve the performance even when applying imbalanced real option data. In particular, the framework fuses simulated big data, known as distilled data, obtained using various traditional parametric methods. The proposed model employs the following three-stage training approach: Our model is pre-trained using big distilled data after it is fine-tuned using real option data through transfer learning. Finally, a delta branch is added to the model and trained. We experimentally evaluated the proposed method using three sets of real option data, namely S&P 500 European call options, EuroStoxx50 call options, and Hang Seng Index put options. Our experimental results on option pricing demonstrate that our proposed model outperforms parametric methods and other machine learning methods. Specifically, our model, which uses pre-training with distilled data, reduces the overall mean absolute percentage error (MAPE) by more than 50%, compared with that of a deep learning model using only real option data without pre-training.

深度学习的出色表现基于其通过处理大量数据来学习高级功能的能力。这种卓越的性能吸引了研究期权定价的研究人员的注意。但是，期权数据比其他类型的数据更昂贵，更不可访问，并且由于期权的流动性而不平衡。这促使我们提出了一个名为DeepOption的新的期权定价和增量对冲框架。该框架基于深度学习，即使应用不平衡的实物期权数据也可以提高性能。尤其是，框架融合了模拟的大数据，即蒸馏数据，是使用各种传统参数方法获得的。所提出的模型采用以下三个阶段的训练方法：在通过转移学习使用实物期权数据进行微调后，我们使用大量提炼的数据对我们的模型进行了预训练。最后，将增量分支添加到模型中并进行训练。我们使用三组实物期权数据实验性地评估了所提出的方法，即标普500欧洲看涨期权，EuroStoxx50看涨期权和恒生指数看跌期权。我们关于期权定价的实验结果表明，我们提出的模型优于参数化方法和其他机器学习方法。具体而言，我们的模型使用了经过提炼的数据进行预训练，可将总体平均绝对百分比误差（MAPE）降低50％以上，