Improving Option Pricing With The Product Constrained Hybrid Neural Network  

Oleh:

Lajbcygier, P.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 2 (Mar. 2004), page 465-476.
Topik: Hybrid; improving; option pricing; product contrained; hybrid neural network

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.10 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In the past decade, many studies across various financial markets have shown conventional option pricing models to be inaccurate. To improve their accuracy, various researchers have turned to artificial neural networks (ANN s). In this work a neural network is constrained in such a way that pricing must be rational at the option - pricing boundaries. The constraints serve to change the regression surface of the ANN so that option pricing accuracy is improved in the locale of the boundaries. These constraints lead to statistically and economically significant out - performance, relative to both the most accurate conventional and nonconventional option pricing models.

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