A neural network versus Black–Scholes: a comparison of pricing and hedging performances

An Erratum has been published for this article in Journal of Forecasting 22(6‐7) 2003, 551 The Black–Scholes formula is a well‐known model for pricing and hedging derivative securities. It relies, however, on several highly questionable assumptions. This paper examines whether a neural network (MLP) can be used to find a call option pricing formula better corresponding to market prices and the properties of the underlying asset than the Black–Scholes formula. The neural network method is applied to the out‐of‐sample pricing and delta‐hedging of daily Swedish stock index call options from 1997 to 1999. The relevance of a hedge‐analysis is stressed further in this paper. As benchmarks, the Black–Scholes model with historical and implied volatility estimates are used. Comparisons reveal that the neural network models outperform the benchmarks both in pricing and hedging performances. A moving block bootstrap is used to test the statistical significance of the results. Although the neural networks are superior, the results are sometimes insignificant at the 5% level. Copyright © 2003 John Wiley & Sons, Ltd.     

对西方数学直观的评析

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具有(F(V×V),R,→,■)模糊逻辑模型的稳定性结构

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Pragmatism in Economic Methodology: The Duhem-Quine Thesis Revisited

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一种支持向量回归中超参数自适应方法

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用事件-条件-动作规则表示模糊知识的方法

提出了模糊触发器的概念，采用模糊事件、模糊条件和模糊动作来对触发器进行模糊推理，为在主动数据库及其应用领域表达不精确的应用语义特性提供了可行的方法．通过一个“过热警报”的应用实例说明了模糊事件-条件一动作(ECA)触发器的建立及其实现方法．     

基于广义归纳逻辑因果模型的知识发现与算法实现

以语言场、广义细胞自动机和广义归纳逻辑因果模型为理论依据，分析了广义因果联系类知识的发现机理，给出了因果联系类知识发现的实现算法。该算法为解决具有随机不确定和模糊不确定性特征的因果联系类知识的发现提供了行之有效的方法。通过算法的运行实例，验证和说明了算法的正确性和有效性。