论文标题
数据驱动的选项定价使用单一和多资产监督学习
Data-Driven Option Pricing using Single and Multi-Asset Supervised Learning
论文作者
论文摘要
我们提出了三种不同的数据驱动方法，用于使用监督的机器学习算法来定价欧洲式的呼叫选项。这些方法产生的模型可提供一系列公平的价格而不是单个价格点。模型的性能在两个股票市场指数上进行了测试：Nifty $ 50 $和印度股票市场的债券。尽管历史和暗示的波动率都不被用作输入，但结果表明，受过训练的模型能够比所有实验更好地捕获选项定价机制或类似于黑色 - choles公式。我们选择的无标度I/O使我们能够使用金融市场多个不同资产的合并数据来培训模型。这不仅允许模型获得更好的概括和预测能力，而且还解决了数据稀少的问题，这是使用机器学习技术的主要局限性。我们还说明了训练有素的模型在2020年股票市场崩溃（2019年1月至2020年4月）之前的性能。
We propose three different data-driven approaches for pricing European-style call options using supervised machine-learning algorithms. These approaches yield models that give a range of fair prices instead of a single price point. The performance of the models are tested on two stock market indices: NIFTY$50$ and BANKNIFTY from the Indian equity market. Although neither historical nor implied volatility is used as an input, the results show that the trained models have been able to capture the option pricing mechanism better than or similar to the Black-Scholes formula for all the experiments. Our choice of scale free I/O allows us to train models using combined data of multiple different assets from a financial market. This not only allows the models to achieve far better generalization and predictive capability, but also solves the problem of paucity of data, the primary limitation of using machine learning techniques. We also illustrate the performance of the trained models in the period leading up to the 2020 Stock Market Crash (Jan 2019 to April 2020).
