Type of Document	Dissertation
Author	Bayazit, Dervis
Author's Email Address	dbayazit@math.fsu.edu
URN	etd-05072010-170123
Title	Sensitivity Analysis of Options under Lévy Processes via Malliavin Calculus
Degree	Doctor of Philosophy
Department	Mathematics, Department of
Advisory Committee	Advisor Name Title Craig A. Nolder Committee Chair Betty Anne Case Committee Member David Kopriva Committee Member Giray Ökten Committee Member Jack Quine Committee Member Fred Huffer University Representative
Keywords	Centered Finite Difference Monte-Carlo simulations FFT Malliavin calculus Inverse Fourier Transform method Normal Inverse Gaussian process Approximation of Lévy processes Variance Gamma process Greeks
Date of Defense	2010-04-12
Availability	unrestricted
Abstract The sensitivity analysis of options is as important as pricing in option theory since it is used for hedging strategies, hence for risk management purposes. This dissertation presents new sensitivities for options when the underlying follows an exponential Lévy process, specifically Variance Gamma and Normal Inverse Gaussian processes. The calculation of these sensitivities is based on a finite dimensional Malliavin calculus and the centered finite difference method via Monte-Carlo simulations. We give explicit formulas that are used directly in Monte-Carlo simulations. By using simulations, we show that a localized version of the Malliavin estimator outperforms others including the centered finite difference estimator for the call and digital options under Variance Gamma and Normal Inverse Gaussian processes driven option pricing models. In order to compare the performance of these methods we use an inverse Fourier transform method to calculate the exact values of the sensitivities of European call and digital options written on S&P 500 index. Our results show that a variation of localized Malliavin calculus approach gives a robust estimator while the convergence of centered finite difference method in Monte-Carlo simulations varies with different Greeks and new sensitivities that we introduce. We also discuss an approximation method for the Variance Gamma process. We introduce new random number generators for the path wise simulations of the approximating process. We improve convergence results for a type of sensitivity by using a mixed Malliavin calculus on the increments of the approximating process.
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