扩展了金融期权定价的数据驱动人工神经网络模型的特征集

Luis Montesdeoca, M. Niranjan
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引用次数: 12

摘要

在金融市场上交易的衍生合同,如期权的价格,预计将与标的资产价值的波动、合同的到期时间和行使类型以及其他宏观经济变量有着复杂的关系。Hutchinson, Lo和Poggio在1994年表明,可以训练非参数人工神经网络来近似这种复杂的函数关系。这里,我们考虑这个模型与期权定价相关的额外输入,并表明近似的准确性确实可以提高。我们考虑交易量、历史波动率、观察到的利率以及这些因素的组合作为附加特征。除了给出包含这些变量如何帮助预测期权价格的实证结果外,我们还分析了以波动性和交易量为输入的不同模型的预测误差,并报告了它们的贡献之间有趣的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extending the feature set of a data-driven artificial neural network model of pricing financial options
Prices of derivative contracts, such as options, traded in the financial markets are expected to have complex relationships to fluctuations in the values of the underlying assets, the time to maturity and type of exercise of the contracts as well as other macroeconomic variables. Hutchinson, Lo and Poggio showed in 1994 that a non-parametric artificial neural network may be trained to approximate this complex functional relationship. Here, we consider this model with additional inputs relevant to the pricing of options and show that the accuracy of approximation may indeed be improved. We consider volume traded, historic volatility, observed interest rates and combinations of these as additional features. In addition to giving empirical results on how the inclusion of these variables helps predicting option prices, we also analyse prediction errors of the different models with volatility and volume traded as inputs, and report an interesting correlation between their contributions.
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