Application of Operator Splitting Methods in Finance

Financial derivatives pricing aims to find the fair value of a financial contract on an underlying asset. Here we consider option pricing in the partial differential equations framework. The contemporary models lead to one-dimensional or multidimensional parabolic problems of the convection-diffusion type and generalizations thereof. An overview of various operator splitting methods is presented for the efficient numerical solution of these problems. Splitting schemes of the Alternating Direction Implicit (ADI) type are discussed for multidimensional problems, e.g. given by stochastic volatility (SV) models. For jump models Implicit-Explicit (IMEX) methods are considered which efficiently treat the nonlocal jump operator. For American options an easy-to-implement operator splitting method is described for the resulting linear complementarity problems. Numerical experiments are presented to illustrate the actual stability and convergence of the splitting schemes. Here European and American put options are considered under four asset price models: the classical Black-Scholes model, the Merton jump-diffusion model, the Heston SV model, and the Bates SV model with jumps

Pricing American-style Derivatives under the Heston Model Dynamics: A Fast Fourier Transformation in the Geske–Johnson Scheme

Theoretical research on option valuation tends to focus on pricing the plain-vanilla European-style derivatives. Duffie, Pan, and Singleton (Econometrica, 2000) have recently developed a general transform method to determine the value of European options for a broad class of the underlying price dynamics. Contrastingly, no universal and analytically attractive approach to pricing of American-style derivatives is yet available. When the underlying price follows simple dynamics, literature suggests using finite difference methods. Simulation methods are often applied in more complicated cases. This paper addresses the valuation of American-style derivatives when the price of an underlying asset follows the Heston model dynamics (Rev.Fin.S., 1993). The model belongs to the class of stochastic volatility models, which have been proposed in the hope of remedying the strike-price biases of the Black–Scholes formula. Option values are obtained by a variant of the Geske–Johnson scheme (JF, 1984), which has been devised in the context of the Black–Scholes model. The scheme exploits the fact that an American option is the limit of a sequence of “Bermudan†derivatives. The latter ones can be priced recursively according to a simple formula, and iterations start from valuing a corresponding European-style security. To implement the recursion, one needs to obtain the expected value of “Bermudan†prices in the joint measure of the state variables of the model. Since the joint density must be, in turn, recovered by inverting the joint characteristic function, an unmodified Geske–Johnson algorithm implies a computationally unfeasible multiple integration. To drastically reduce the cost of numerical integration, I suggest applying a kernel-smoothed bivariate fast Fourier transformation to obtain the density function. Numerical accuracy of the method is assessed by predicting option prices of the S&P 100 index optionsAmerican-style option, stochastic volatility model, Geske–Johnson scheme, characteristic function inversion, fast Fourier transform

Option Pricing: The empirical tests of the Black-Scholes pricing formula and the feed-forward networks

In this article we evaluate the pricing performance of the rather simple but revolutionary Black-Scholes model and one of the more complex techniques (neural networks) on the European-style S&P Index call and put options over the period of 1.6.2006 till 8.6.2007. Our results on call options show that generally Black-Scholes model performs better than simple generalized feed-forward networks. On the other hand neural networks performance is improving as the option goes deep in the money and as days to expiration increase, compared to the worsening performance of the BS models. Neural networks seem to correct for the well-known Black-Scholes model moneyness and maturity biases.option pricing, neural networks