提前执行取代运行时:神经网络预测期权波动

Proceedings of the Tenth Conference on Artificial Intelligence for Applications Pub Date : 1994-03-01 DOI:10.1109/CAIA.1994.323630

M. Malliaris, L. Salchenberger

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引用次数: 4

摘要

比较了三种估计每日标准普尔100指数股票市场期权波动率的方法。通过Black-Scholes模型计算的隐含波动率是目前最流行的估计波动率的方法，被交易者用于期权定价。历史波动率已被用来预测隐含波动率，但估计是较差的预测。神经网络预测波动率的效果远远优于历史方法。

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Do-ahead replaces run-time: a neural network forecasts options volatility

Compares three methods of estimating the volatility of daily S&P 100 Index stock market options. The implied volatility, calculated via the Black-Scholes model, is currently the most popular method of estimating volatility and is used by traders in the pricing of options. Historical volatility has been used to predict the implied volatility, but the estimates are poor predictors. A neural network for predicting volatility is shown to be far superior to the historical method.<>

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Proceedings of the Tenth Conference on Artificial Intelligence for Applications

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