深度学习模型在巴西石油股期权定价上显著优于经典Black-Scholes模型。
Deep Learning vs. Black-Scholes: Option Pricing Performance on Brazilian Petrobras Stocks
- 用残差网络结合市场与解析定价构建混合损失函数。
- 测试集上均方误差降低64.3%，长周期表现更稳定。
- 适合关注金融衍生品建模的量化研究者和从业者。
本文研究基于深度残差网络的欧式期权定价方法在巴西石油公司（Petrobras）股票上的应用，并与Black-Scholes（BS）模型进行对比。利用2016年11月至2025年1月共八年历史数据，通过网络爬虫从B3（巴西证券交易所）获取，采用80-20划分训练与测试集，测试集包含2025年11月、12月和1月的数据。通过自研混合损失函数训练深度学习模型，结果表明，在3-19雷亚尔（BRL）价格区间内，该模型相比黑斯科尔斯模型平均绝对误差降低64.3%。此外，不同于黑斯科尔斯模型随到期时间延长而精度下降，深度学习模型在长期限合约中仍保持较高准确性。研究揭示了深度学习在金融建模中的潜力，未来工作将聚焦于不同价格区间的专用模型优化。
原文摘要 · Abstract (English)
This paper explores the use of deep residual networks for pricing European options on Petrobras, one of the world's largest oil and gas producers, and compares its performance with the Black-Scholes (BS) model. Using eight years of historical data from B3 (Brazilian Stock Exchange) collected via web scraping, a deep learning model was trained using a custom built hybrid loss function that incorporates market data and analytical pricing. The data for training and testing were drawn between the period spanning November 2016 to January 2025, using an 80-20 train-test split. The test set consisted of data from the final three months: November, December, and January 2025. The deep residual network model achieved a 64.3\% reduction in the mean absolute error for the 3-19 BRL (Brazilian Real) range when compared to the Black-Scholes model on the test set. Furthermore, unlike the Black-Scholes solution, which tends to decrease its accuracy for longer periods of time, the deep learning model performed accurately for longer expiration periods. These findings highlight the potential of deep learning in financial modeling, with future work focusing on specialized models for different price ranges.
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