Abstract. Hilbert space-valued jump-diffusion models are employed for various markets and derivatives. Examples include swaptions, which depend on continuous forward curves, and basket options on stocks. Usually, no analytical pricing formulas are available for such products. Numerical methods, on the other hand, suffer from exponentially increasing computational effort with increasing dimension of the problem, the "curse of dimension." In this paper, we present an efficient approach using partial integro-differential equations. The key to this method is a dimension reduction technique based on a Karhunen-Loève expansion, which is also known as proper orthogonal decomposition. Using the eigenvectors of a covariance operator, the differential equation is projected to a low-dimensional problem. Convergence results for the projection are given, and the numerical aspects of the implementation are discussed. An approximate solution is computed using a sparse grid combination technique and discontinuous Galerkin discretization. The main goal of this article is to combine the different analytical and numerical techniques needed, presenting a computationally feasible method for pricing European options. Numerical experiments show the effectiveness of the algorithm.
Abstract. Hilbert space-valued jump-diffusion models are employed for various markets and derivatives. Examples include swaptions, which depend on continuous forward curves, and basket options on stocks. Usually, no analytical pricing formulas are available for such products. Numerical methods, on the other hand, suffer from exponentially increasing computational effort with increasing dimension of the problem, the "curse of dimension." In this paper, we present an efficient approach using partial integro-differential equations. The key to this method is a dimension reduction technique based on a Karhunen-Loève expansion, which is also known as proper orthogonal decomposition. Using the eigenvectors of a covariance operator, the differential equation is projected to a low-dimensional problem. Convergence results for the projection are given, and the numerical aspects of the implementation are discussed. An approximate solution is computed using a sparse grid combination technique and discontinuous Galerkin discretization. The main goal of this article is to combine the different analytical and numerical techniques needed, presenting a computationally feasible method for pricing European options. Numerical experiments show the effectiveness of the algorithm.
“…However, this is necessary in case the formula is used to find the generator of a Markov process. In this article, we provide an improvement of the work of [14]. Even if the methods are similar, the current article is presented in a clearer and direct way, due to integrands having general integrability conditions.…”
Section: Introductionmentioning
confidence: 99%
“…In this context, Itô's formula was originally given in [14]. However, there it was only proven for a smaller class of integrands, as stochastic integration for Banach space valued integrands was still not understood in the generality of the forthcoming papers [9], [10].…”
Section: Introductionmentioning
confidence: 99%
“…Even if the methods are similar, the current article is presented in a clearer and direct way, due to integrands having general integrability conditions. The mark space (E, E), associated to the compensated Poisson random measure, is also allowed to be more general than in [14], where E has been a separable Banach space. We refer Remark 2.1 and Remark 3.2, where this generalization is discussed.…”
Abstract. We prove Itô's formula for a general class of functions H : R + × F → G of class C 1,2 , where F, G are separable Banach spaces, and jump processes driven by a compensated Poisson random measure.
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