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In this paper, we propose and develop an iterative method to calculate a limiting probability distribution vector of a transition probability tensorarising from a higher order Markov chain. In the model, the computation of such limiting probability distribution vectorcan be formulated as a-eigenvalue problemassociated with the eigenvalue 1 ofwhere all the entries ofare required to be non-negative and its summation must be equal to one. This is an analog of the matrix case for a limiting probability vector of a transition probability matrix arising from the first-order Markov chain. We show that ifis a transition probability tensor, then solutions of this-eigenvalue problem exist. Whenis irreducible, all the entries of solutions are positie. With some suitable conditions of, the limiting probability distribution vector is even unique. Under the same uniqueness assumption, the linear convergence of the iterative method can be established. Numerical examples are presented to illustrate the theoretical results of the proposed model and the iterative method. [ABSTRACT FROM AUTHOR]

In the present paper, we convert the usual n-step backward recursion that arises in option pricing into a set of independent integral equations by using a z-transform approach. In order to solve these equations, we consider different quadrature procedures that transform the integral equation into a linear system that we solve by iterative algorithms and we study the benefits of suitable preconditioning techniques. We show the relevance of our procedure in pricing options (such as plain vanilla, lookback, single and double barrier options) when the underlying evolves according to an exponential Lévy process. [ABSTRACT FROM AUTHOR]