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- W95015212 abstract "This thesis investigates the merits of the artificial neural network, a nonparametric option pricing method. The valuation and hedging performance on European-style options is assessed through simulation experiments and an application to a sample of ASX index options. Recent evidence documented by Amin, Coval and Seyhun (2004) suggests that stock market momentum exerts a systematic influence on option prices; this relationship is examined by integrating momentum into the neural network. In the simulation where Black-Scholes is the theoretically correct model, the pricing and hedging performance of the artificial neural network is found to only slightly underperform Black-Scholes. However, the neural network demonstrates its superiority over Black-Scholes in the application to ASX index options; significant improvements are realised in both pricing and hedging performance. Past market returns are also found to systematically affect observed option prices; option prices are found to be significantly inflated following negative market momentum relative to an identical option following positive momentum. Incorporating momentum into the neural network successfully controls for this pricing bias, but does not improve hedging performance." @default.
- W95015212 created "2016-06-24" @default.
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- W95015212 date "2005-01-01" @default.
- W95015212 modified "2023-09-27" @default.
- W95015212 title "Pricing and hedging ASX index options using neural networks" @default.
- W95015212 hasPublicationYear "2005" @default.
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