The role of Advanced Option Pricing Techniques Empirical tests of Neural Networks
|Author:||Mgr. Jiří Brejcha|
|Year:||2011 - winter|
|Leaders:|| doc. PhDr. Jozef Baruník Ph.D.
|Work type:|| Finance, Financial Markets and Banking
|Awards and prizes:|
|Abstract:||This thesis concerns with a comparison of two advanced option-pricing techniques applied on European-style DAX index options. Specifically, the study examines the performance of both the stochastic volatility model based on asymmetric nonlinear GARCH, which was proposed by Heston and Nandi (2000), and the artificial neural network, where the conventional Black-Scholes-Merton model serves as a benchmark. These option-pricing models are tested with the use of the dataset covering the period 3rd July 2006 – 30th October 2009 as well as of its two subsets labelled as “before crisis” and “in crisis” data where the breakthrough day is the 17th March 2008. Finding the most appropriate option-pricing method for the whole periods as well as for both the “before crisis” and the “in crisis” datasets is the main focus of this work. The first two chapters introduce core issues involved in option pricing, while the subsequent third section provides a theoretical background related to all of above-mentioned pricing methods. At the same time, the reader is provided with an overview of the theoretical frameworks of various nonlinear optimization techniques, i.e. descent gradient, quassi-Newton method, Backpropagation and Levenberg-Marquardt algorithm. The empirical part of the thesis then shows that none of the models would outperform the others in all examined data categories. However, we may conclude that the neural network approach gives the relatively best results as an option pricing tool when compared to the Black-Scholes-Merton model and to the Heston and Nandi (2000) method.|