利用人工神经网络在经典和多曲线框架下逼近期权希腊文

Ryno du Plooy, Pierre J. Venter
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引用次数: 0

摘要

本文提出使用人工神经网络(ANN),在经典和现代多曲线框架内近似分析约翰内斯堡证券交易所(JSE)40 强欧式看涨期权的期权价格敏感性。考虑到南非市场流动性差的特点,在人工生成的期权价格数据上对 ANNs 进行了训练,并使用由已公布的波动率偏差构建的隐含波动率曲面对优化后的 ANNs 的样本外性能进行了评估。本文的研究结果表明,在南非市场的实际样本外应用中,根据人工生成的输入数据训练的方差网络能够准确地近似经典和现代多曲线框架各自期权价格敏感性的显式解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approximating Option Greeks in a Classical and Multi-Curve Framework Using Artificial Neural Networks
In this paper, the use of artificial neural networks (ANNs) is proposed to approximate the option price sensitivities of Johannesburg Stock Exchange (JSE) Top 40 European call options in a classical and a modern multi-curve framework. The ANNs were trained on artificially generated option price data given the illiquid nature of the South African market, and the out-of-sample performance of the optimized ANNs was evaluated using an implied volatility surface constructed from published volatility skews. The results from this paper show that ANNs trained on artificially generated input data are able to accurately approximate the explicit solutions to the respective option price sensitivities of both a classical and a modern multi-curve framework in a real-world out-of-sample application to the South African market.
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