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Option pricing: The empirical tests of the black-scholes pricing formula and the feed-forward network

机译:期权定价:黑色学者定价公式和前馈网络的实证检验

摘要

In this article we evaluate the pricing performance of the rather simple but revolutionary Black-Scholes model and one of the more complex techniques (neural networks) on the European-style S&P Index call and put options over the period of 1.6.2006 till 8.6.2007. Our results on call options show that generally Black-Scholes model performs better than simple generalized feed-forward networks. On the other hand neural networks performance is improving as the option goes deep in the money and as days to expiration increase, compared to the worsening performance of the BS models. Neural networks seem to correct for the well-known Black-Scholes model moneyness and maturity biases.
机译:在本文中,我们评估了较为简单但具有革命性的Black-Scholes模型以及欧洲标准普尔指数认购权中较复杂的技术之一(神经网络)的定价表现,并在2006年6月1.6日至8.6期间发行了认沽期权。 2007年。我们的看涨期权结果表明,一般而言,Black-Scholes模型的性能要优于简单的广义前馈网络。另一方面,与BS模型的性能下降相比,随着选择权的增加和到期日的增加,神经网络的性能也在提高。神经网络似乎可以纠正著名的Black-Scholes模型的货币和成熟度偏差。

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