Work detail

Artificial Neural Networks in Option Pricing

Author: Mgr. Bc. Dominik Vach
Year: 2019 - winter
Leaders: PhDr. Petr Gapko Ph.D.
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 86
Awards and prizes:
Link: https://is.cuni.cz/webapps/zzp/detail/191717/
Abstract: This thesis examines the application of neural networks in the context of
option pricing. Throughout the thesis, different architecture choices and
prediction parameters are tested and compared in order to achieve better
performance and higher accuracy in option valuation. Two different volatility
forecast mechanisms are used to compare neural networks performance with
Black Scholes parametric model. Moreover, the performance of a neural
network is compared also to more advanced modular neural networks. A
new technique of adding rational prediction assumptions to neural network
prediction is tested and the thesis shows the importance of adding virtual
options fulfilling these assumptions in order to achieve better training of the
neural network. This method comes out to increase the prediction power of
the network significantly. The thesis also shows the neural network prediction
outperforms the traditional parametric methods. The size and number of
hidden layers in a neural network is tested with an emphasis to provide a
benchmark and a structured way how to choose neural network parameters
for future applications in option pricing.

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