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BLACK-SCHOLES VERSUS ARTIFICIAL NEURAL NETWORKS IN PRICING FTSE 100 OPTIONS

机译:定价FTSE 100期权中的黑洞与人工神经网络

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This paper compares the performance of Black-Scholes with an artificial neural network (ANN) in pricing European-style call options on the FTSE 100 index. It is the first extensive study of the performance of ANNs in pricing UK options, and the first to allow for dividends in the closed-form model. For out-of-the-money options, the ANN is clearly superior to Black-Scholes. For in-the-money options, if the sample space is restricted by excluding deep in-the-money and long maturity options (3.4% of total volume), then the performance of the ANN is comparable to that of Black-Scholes. The superiority of the ANN is a surprising result, given that European-style equity options are the home ground of Black-Scholes, and suggests that ANNs may have an important role to play in pricing other options for which there is either no closed-form model, or the closed-form model is less successful than is Black-Scholes for equity options.
机译:本文将Black-Scholes与人工神经网络(ANN)的性能进行比较,以FTSE 100指数定价欧式期权。这是对ANN​​在英国期权定价中的表现进行的首次广泛研究,也是第一个在封闭式模型中考虑股息的情况。对于价外期权,ANN明显优于Black-Scholes。对于价内期权,如果样本空间由于排除价内和长期到期期权(占总交易量的3.4%)而受到限制,则ANN的性能可与Black-Scholes媲美。鉴于欧式股票期权是布莱克-斯科尔斯的故乡,因此人工神经网络的优越性是一个令人惊讶的结果,并表明人工神经网络在定价没有封闭形式的其他期权时可能发挥重要作用模型或封闭形式的模型不如Black-Scholes的股票期权成功。

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